EP2309491A1 - System and method for activating functions including a first function and a second function - Google Patents
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- EP2309491A1 EP2309491A1 EP10007096A EP10007096A EP2309491A1 EP 2309491 A1 EP2309491 A1 EP 2309491A1 EP 10007096 A EP10007096 A EP 10007096A EP 10007096 A EP10007096 A EP 10007096A EP 2309491 A1 EP2309491 A1 EP 2309491A1
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- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000003213 activating effect Effects 0.000 title claims abstract description 8
- 230000004913 activation Effects 0.000 claims abstract description 39
- 230000007704 transition Effects 0.000 claims description 9
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- 238000012545 processing Methods 0.000 description 3
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/36—Input/output arrangements for on-board computers
- G01C21/3605—Destination input or retrieval
- G01C21/3608—Destination input or retrieval using speech input, e.g. using speech recognition
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/26—Speech to text systems
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
- G10L2015/226—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics
- G10L2015/228—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics of application context
Definitions
- the present invention relates generally to automatic speech recognition, and more particularly to automatic speech recognition for a particular context.
- ASR Automatic Speech Recognition
- the object of automatic speech recognition is to acquire an acoustic signal representative of speech, i.e., speech signals, and determine the words that were spoken by pattern matching.
- Speech recognizers typically have a set of stored acoustic and language models represented as patterns in a computer database. These models are then compared to the acquired signals.
- the contents of the computer database, how the database is trained, and the techniques used to determine the best match are distinguishing features of different types of speech recognition systems.
- Segmental models methods assume that there are distinct phonetic units, e.g., phonemes, in spoken language that can be characterized by a set of properties in the speech signal over time.
- Input speech signals are segmented into discrete sections in which the acoustic properties represent one or more phonetic units and labels are attached to these regions according to these properties.
- a valid vocabulary word consistent with the constraints of the speech recognition task, is then determined from the sequence of assigned phonetic labels.
- Template-based methods use the speech patterns directly without explicit feature determination and segmentation.
- a template-based speech recognition system is initially trained using known speech patterns. During recognition, unknown speech signals are compared with each possible pattern acquired during the training and classified according to how well the unknown patterns match the known patterns.
- Hybrid methods combine certain features of the above-mentioned segmental model and template-based methods.
- more than just acoustic information is used in the recognition process.
- neural networks have been used for speech recognition.
- a pattern classifier detects the acoustic feature vectors and convolves vectors with filters matched to the acoustic features and sums up the results over time.
- ASR enabled systems include two major categories, i.e., information retrieval (IR) systems, and command and control (CC) systems.
- IR information retrieval
- CC command and control
- the information retrieval (IR) system searches content stored in a database based on a spoken query.
- the content can include any type of multimedia content such as, but not limited to, text, images, audio and video.
- the query includes key words or phrases.
- Many IR systems allow the user to specify additional constraints to be applied during the search. For instance, a constraint can specify that all returned content has a range of attributes.
- the query and the constraints are specified as text.
- textual input and output is difficult, if not impossible.
- applications include, for example, searching a database while operating a machine, or a vehicle, or applications with a limited-functionality keyboard or display, such as a telephone.
- ASR enabled IR systems are preferred.
- ASR enabled CC systems recognize and interpret spoken commands into machine understandable commands. Non limited examples of the spoken commands are "call” a specified telephone number, or "play” a specified song.
- a number of the ASR enabled CC systems have been developed due to recent advancements in speech recognition software. Typically, those systems operate in particular environment using a particular context for the spoken commands.
- U.S. Patent No. 4,989,253 discloses an ASR enabled system for moving and focusing a microscope. That system uses the context associated with microscopes.
- U.S. Patent No. 5,970,457 discloses an ASR enabled system for operating medical equipment, such as surgical tools, in accordance with the spoken commands associated with appropriate context.
- ASR enabled systems need to include multiple vocabularies and language models useful for different contexts.
- Such systems are usually configured to activate appropriate vocabulary and language model based on a particular context of interest selected by a user.
- the context of the ASR enabled system is, but not limited to, a vocabulary, language model, a grammar, domain, database, and/or subsystem with related contextual functionality.
- the functionalities related to music, contacts, restaurants, or points of historical interest would each have separate and distinguishable contexts.
- the ASR enabled system that utilizes multiple contexts is a contextual ASR enabled system.
- ASR systems that distinguish intended speech input from background noise, or background speech.
- Always-listening systems employ a lexical analysis of the recognized audio signal to detect keywords, e.g., "computer,” which are intended to activate the ASR enabled systems for further input.
- ASR enabled system makes use of other input clues modeled after human-to-human discourse, such as direction of gaze.
- a PTT control e.g., a button
- SNR signal-to-noise ratio
- an embedded ASR system is one in which all speech signal processing necessary to perform CC or IR takes place on a device, typically having an attached wired or wireless microphone. Some of the data required to generate, modify, or activate the embedded ASR system can be downloaded from different devices via wired or wireless data channels. However, at the time of ASR processing, all data resides in a memory associated with the device.
- ASR systems such as IR and CC systems
- IR and CC systems in conjunction with a particular context or a plurality of contexts.
- some embedded ASR systems have limitations which do not necessarily apply to desktop or server-based ASR systems.
- desktop or server-based systems might be able to process a music-retrieval instruction, such as searching for a particular artist, from any state of the system.
- the embedded ASR system e.g., an ASR system in a vehicle, might require the user to switch to an appropriate contextual state first, and would allow the user to provide the speech input relevant only to that particular contextual state.
- the embedded ASR system is associated with multiple different contexts.
- music can be one context.
- the embedded ASR system is in the music context state, the system expects user speech input to be relevant to music, and the system is configured to execute functions only relevant to retrieving music.
- Navigation and contact are other non limited examples of the context of the ASR system.
- the user has to push the PTT button, pronounce a contextual instruction, e.g., a code word such as "music,” to switch the ASR system into a music contextual state.
- a contextual instruction e.g., a code word such as "music”
- the user can input a spoken instruction for the music retrieval. If the user inputs music-related spoken instructions, while in some other contextual state, the ASR system fails.
- Figure 1 shows a conventional embedded ASR system. After a PTT button 105 is pressed, the system is expecting speech input containing contextual instructions 110-112. After recognizing 120 the contextual instruction, the system transitions to an appropriate contextual state 130-132. Accordingly, the system after recognizing a subsequent speech input 133-135 activates appropriate function 136-138.
- a method and a system for activating functions including a first function and a second function, wherein the system is embedded in an apparatus are disclosed.
- the system includes a control configured to be activated by multiple activation styles, wherein the control generates a signal indicative of a particular activation style from the plurality of activation styles; and a controller configured to activate either the first function or the second function based on the particular activation style, wherein the first function is configured to be executed based only on the activation style, and wherein the second function is further configured to be executed based on a speech input.
- Alternative embodiment describes the method for activating a first function and a second function, comprising the steps of providing a control configured to be activated by multiple activation styles, wherein the control generates a signal indicative of a particular activation style from the plurality of activation styles; activating either the first function or the second function based on the particular activation style, wherein the first function is configured to be executed based only on the activation style, and wherein the second function is further configured to be executed based on a speech input; and executing either the first function or the second function.
- Embodiments of the invention are based on a realization that multiple dedicated contextual push-to-talk (PTT) controls facilitate an activation of appropriate functions in embedded automatic speech recognition (ASR) systems.
- PTT contextual push-to-talk
- ASR embedded automatic speech recognition
- FIG. 2 shows the embedded ASR system according one embodiment of the invention.
- the system includes a processor 201, which includes a memory 202, input/output interfaces, and signal processors as known in the art.
- the system 200 includes multiple states 231-233 stored in the memory 202.
- each state is associated with a particular context.
- one state is associated with music context, and another state is associated with contact context.
- Each state is also associated with at least one function of functions 237-239.
- the functions 237-239 are configured to be activated based on speech inputs 233-235.
- the functions are associated with the state in a manner similar to the association of the context with the state. For example, functions configured to select and play music are associated with the state associated with the music context. But functions configured to select and call to a particular phone number, are associated with the state associated with the contact context.
- the speech input includes an identifier of the function and a parameter of the function to be executed.
- the speech input is "Call Joe.”
- the identifier of the function is "Call" part of the input.
- the function for executing telephone calls is selected from the multiple functions associated with the "telephone" state.
- the "Joe" part of the speech input is the parameter to the function selected based on the identifier. Accordingly, the system executes selected function using the parameter, i.e., call to a telephone number selected from a phonebook based on the name "Joe.”
- the system 200 is configured to activate a function associated with the state, only when the system is transitioned into that state. For example, in order to activate a music function, the system has to be first transitioned into the state associated with the music function, and, accordingly, associated with the music context.
- the system 200 provides a control panel 210, which includes multiple controls 221-223, e.g., contextual PTT controls.
- Each contextual PTT control can be any input control configured to be activated tangibly, such as a button, a joystick, or a touch-sensitive surface.
- Each contextual PTT control 221-223 has one to one correspondence with the states 231-233.
- the contextual PTT controls Upon activation, the contextual PTT controls generate signals 242-244.
- the signal can be any type of signal, e.g., a binary signal, which carries information about activated contextual PTT control.
- a state transition module 220 upon receiving the signal, transitions the system 200 into the state associated with the signal to activate the function.
- the transition into the state is accomplished by associating 256 a data model from a set of data models 255 with an ASR engine 250.
- the data model includes a vocabulary, and/or a set of predetermined commands or search terms, which allows the ASR engine to interpret the speech inputs.
- the ASR engine interprets the speech inputs 233-235 into inputs 261-263 expected by the functions 237-239. Accordingly, if the data model 256 includes vocabulary of, e.g., music context, then the ASR engine can interpret only music related speech input 234.
- the state transition module preselects, e.g., uploads into memory of processor 201, the functions included into the corresponding state.
- the embodiments provide significant advantages over conventional systems with a single PTT button.
- the conventional systems require additional speech input to transition into a particular state.
- the embodiments of the invention directly transition the system into the state associated with the control based on the activation of that control.
- the system 200 in contrast with conventional systems, takes advantage of muscle memory, which is enhanced by repeated similar movements, similar to touch typing and gear shifting. Therefore, the controls are arranged so the user can activate the controls with minimal distraction from primary tasks, e.g., driving a vehicle.
- each control conveys an identifier 225-227 of the context associated with the state.
- the identifier can have a caption rendered on the control with a name of the context such as "call,” or "music.”
- the identifier can be a color of the control, a shape of the control, a location of the control on the device, and a combination thereof. This embodiment reduces training time usually required for a human operator to learn how to operate the embedded ASR system.
- the system 200 can be embedded in an instrumental panel 410 of a vehicle 400.
- Contextual PTT controls 432-433 can be arranged on a steering wheel 430.
- contextual PTT controls 425 can be place on a control module 420. The multiple contextual PTT controls simplify the search, and require less user interaction so that the user can concentrate on operating the vehicle.
- FIG. 3 shows a block diagram of a system and method 300 according to another embodiment of the invention.
- a control 310 is a multi-purpose PTT control connected via a controller 320 to at least functions 330 and 340.
- the control 310 is configured to generate a signal indicative of a particular activation style 315 selected from multiple activation styles 317.
- the activation styles include, e.g., a single click, a double click, and press and hold activation styles.
- the controller 320 activates 325 either a first function 340 or a second function 330 based on the particular activation style 315.
- the second function 330 requires a speech-enabled activation, i.e., is further configured to expect speech input 333.
- This embodiment enables utilization of any conventional control as the multi-purpose PTT control. If the user activates the control in a "normal" activation style, e.g., single click, then the system activates 342 and execute 344 the first function. Otherwise, the user activates the control with a "special" activation style, e.g. double click, invoking function 337 which expects the speech input 333.
- a "normal" activation style e.g., single click
- a single click on a green call button on a telephone displays recent calls.
- a double click on the same green call button causes the system to detect speech input, e.g., a phonebook search like "John Doe", and execute a "call" function according to the speech input.
- the function 340 is the function that displays the recent calls.
- the function 340 does not need any additional input when activated with the single click activation style.
- the function that calls to a particular phone number is the function 330, which requires an additional input, e.g., a name of a contact from the phonebook. In this embodiment, this additional input is interpreted by the embedded ASR system based on the speech input 333.
- buttons on a radio can accept speech input. If the normal actuation acts as a simple toggle operation, i.e., play or pause, random playback on or off, the speech-enabled actuation detects speech input for the operation, i.e., play what , or shuffle what.
- implementation of the speech-enabled activation of the function 330 is similar to implementation of the states of the system 200.
- the system 300 is transitioned into a state associated with the second function 330, similar to the states 231-233.
- the systems 200 and 300 are combined providing multiple multi-purpose contextual PTT controls.
- the control panel 210 includes multiple multi-purpose PTT controls. This embodiment allows for embedding the ASR system in a device having conventional buttons turning the device into multi-purpose contextual embedded ASR system.
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Abstract
A method and a system for activating functions including a first function and a second function, wherein the system is embedded in an apparatus, are disclosed. The system includes a control configured to be activated by a plurality of activation styles, wherein the control generates a signal indicative of a particular activation style from multiple activation styles; and controller configured to activate either the first function or the second function based on the particular activation style, wherein the first function is configured to be executed based only on the activation style, and wherein the second function is further configured to be executed based on a speech input.
Description
- The present invention relates generally to automatic speech recognition, and more particularly to automatic speech recognition for a particular context.
- The object of automatic speech recognition is to acquire an acoustic signal representative of speech, i.e., speech signals, and determine the words that were spoken by pattern matching. Speech recognizers typically have a set of stored acoustic and language models represented as patterns in a computer database. These models are then compared to the acquired signals. The contents of the computer database, how the database is trained, and the techniques used to determine the best match are distinguishing features of different types of speech recognition systems.
- Various speech recognition methods are known. Segmental models methods assume that there are distinct phonetic units, e.g., phonemes, in spoken language that can be characterized by a set of properties in the speech signal over time. Input speech signals are segmented into discrete sections in which the acoustic properties represent one or more phonetic units and labels are attached to these regions according to these properties. A valid vocabulary word, consistent with the constraints of the speech recognition task, is then determined from the sequence of assigned phonetic labels.
- Template-based methods use the speech patterns directly without explicit feature determination and segmentation. A template-based speech recognition system is initially trained using known speech patterns. During recognition, unknown speech signals are compared with each possible pattern acquired during the training and classified according to how well the unknown patterns match the known patterns.
- Hybrid methods combine certain features of the above-mentioned segmental model and template-based methods. In certain systems more than just acoustic information is used in the recognition process. Also, neural networks have been used for speech recognition. For example, in one such network, a pattern classifier detects the acoustic feature vectors and convolves vectors with filters matched to the acoustic features and sums up the results over time.
- ASR enabled systems include two major categories, i.e., information retrieval (IR) systems, and command and control (CC) systems.
- In general, the information retrieval (IR) system searches content stored in a database based on a spoken query. The content can include any type of multimedia content such as, but not limited to, text, images, audio and video. The query includes key words or phrases. Many IR systems allow the user to specify additional constraints to be applied during the search. For instance, a constraint can specify that all returned content has a range of attributes. Typically, the query and the constraints are specified as text.
- For some applications, textual input and output is difficult, if not impossible. These applications include, for example, searching a database while operating a machine, or a vehicle, or applications with a limited-functionality keyboard or display, such as a telephone. For such applications, ASR enabled IR systems are preferred.
- An example of the ASR enabled IR system is described in
U.S. Patent 7,542,966 , "Method and system for retrieving documents with spoken queries," issued to Wolf et al. on June 2, 2009. - ASR enabled CC systems recognize and interpret spoken commands into machine understandable commands. Non limited examples of the spoken commands are "call" a specified telephone number, or "play" a specified song. A number of the ASR enabled CC systems have been developed due to recent advancements in speech recognition software. Typically, those systems operate in particular environment using a particular context for the spoken commands.
- Large vocabularies and complex language models slow the ASR enabled systems, and require more resources, such as memory and processing. Large vocabularies can also reduce an accuracy of the systems. Therefore, most ASR enabled systems have small vocabularies and simple language models typically associated with a relevant context. For example,
U.S. Patent No. 4,989,253 discloses an ASR enabled system for moving and focusing a microscope. That system uses the context associated with microscopes. Also,U.S. Patent No. 5,970,457 discloses an ASR enabled system for operating medical equipment, such as surgical tools, in accordance with the spoken commands associated with appropriate context. - However, a number of the ASR enabled systems need to include multiple vocabularies and language models useful for different contexts. Such systems are usually configured to activate appropriate vocabulary and language model based on a particular context of interest selected by a user.
- As defined herein, the context of the ASR enabled system is, but not limited to, a vocabulary, language model, a grammar, domain, database, and/or subsystem with related contextual functionality. For example, the functionalities related to music, contacts, restaurants, or points of historical interest would each have separate and distinguishable contexts. The ASR enabled system that utilizes multiple contexts is a contextual ASR enabled system.
- Accordingly, for the contextual ASR enabled systems, it is necessary to specify the context for the spoken queries or the spoken commands.
- There are different types of ASR systems that distinguish intended speech input from background noise, or background speech. Always-listening systems employ a lexical analysis of the recognized audio signal to detect keywords, e.g., "computer," which are intended to activate the ASR enabled systems for further input.
- Another type of the ASR enabled system makes use of other input clues modeled after human-to-human discourse, such as direction of gaze.
- Yet another type of ASR system uses push-to-talk (PTT) functionality. A PTT control, e.g., a button, is used to mark the beginning of a stream of audio signal as intended speech input. In some implementations, the end of the speech input is determined automatically by analyzing, for example, the amplitude or signal-to-noise ratio (SNR) of the acquired signal. In other implementations, the user is required to keep the button depressed until the user is finished speaking, with the release of the button explicitly marking the end of the input signal.
- Sometimes, it is necessary to embed the ASR enabled system directly in a physical device rather than to implement the ASR enabled system on network-based computing resources. Scenarios where such embedding may be necessary include those where persistent network connection cannot be assumed. In those scenarios, even if the ASR enabled system involves updating databases on network computers, it is necessary to obtain information through human-machine interaction conducted independently on the device. Then, after the network communication channel is restored, the updated information collected on the device can be synchronized with the network-based database.
- As defined herein, an embedded ASR system is one in which all speech signal processing necessary to perform CC or IR takes place on a device, typically having an attached wired or wireless microphone. Some of the data required to generate, modify, or activate the embedded ASR system can be downloaded from different devices via wired or wireless data channels. However, at the time of ASR processing, all data resides in a memory associated with the device.
- As described above, it is advantageous to use different types of ASR systems such as IR and CC systems in conjunction with a particular context or a plurality of contexts. Also, due to their limited memory and CPU resources, some embedded ASR systems have limitations which do not necessarily apply to desktop or server-based ASR systems. For example, desktop or server-based systems might be able to process a music-retrieval instruction, such as searching for a particular artist, from any state of the system. However, the embedded ASR system, e.g., an ASR system in a vehicle, might require the user to switch to an appropriate contextual state first, and would allow the user to provide the speech input relevant only to that particular contextual state.
- Typically, the embedded ASR system is associated with multiple different contexts. For example, music can be one context. While the embedded ASR system is in the music context state, the system expects user speech input to be relevant to music, and the system is configured to execute functions only relevant to retrieving music. Navigation and contact are other non limited examples of the context of the ASR system.
- For example, in the embedded ASR system with user interface employing a PTT button, to search for a musical performer, the user has to push the PTT button, pronounce a contextual instruction, e.g., a code word such as "music," to switch the ASR system into a music contextual state. After speaking the code word, the user can input a spoken instruction for the music retrieval. If the user inputs music-related spoken instructions, while in some other contextual state, the ASR system fails.
-
Figure 1 shows a conventional embedded ASR system. After aPTT button 105 is pressed, the system is expecting speech input containing contextual instructions 110-112. After recognizing 120 the contextual instruction, the system transitions to an appropriate contextual state 130-132. Accordingly, the system after recognizing a subsequent speech input 133-135 activates appropriate function 136-138. - However, complex tasks, such as music retrieval and destination entry, interfere with other user operations, e.g., driving a vehicle, especially when durations of the tasks increase. Hence, it is often desired to reduce the number of steps to activate a function with speech input in the embedded ASR system.
- A method and a system for activating functions including a first function and a second function, wherein the system is embedded in an apparatus, are disclosed. In one embodiment, the system includes a control configured to be activated by multiple activation styles, wherein the control generates a signal indicative of a particular activation style from the plurality of activation styles; and a controller configured to activate either the first function or the second function based on the particular activation style, wherein the first function is configured to be executed based only on the activation style, and wherein the second function is further configured to be executed based on a speech input.
- Alternative embodiment describes the method for activating a first function and a second function, comprising the steps of providing a control configured to be activated by multiple activation styles, wherein the control generates a signal indicative of a particular activation style from the plurality of activation styles; activating either the first function or the second function based on the particular activation style, wherein the first function is configured to be executed based only on the activation style, and wherein the second function is further configured to be executed based on a speech input; and executing either the first function or the second function.
-
-
Figure 1 is a block diagram of a conventional automatic speech recognition system; -
Figures 2-3 are block diagrams of embedded automatic speech recognition methods and systems according different embodiments of the invention; and -
Figure 4 is a partial front view of an instrumental panel of a vehicle including the system according some embodiments of the invention. - Embodiments of the invention are based on a realization that multiple dedicated contextual push-to-talk (PTT) controls facilitate an activation of appropriate functions in embedded automatic speech recognition (ASR) systems.
-
Figure 2 shows the embedded ASR system according one embodiment of the invention. The system includes a processor 201, which includes a memory 202, input/output interfaces, and signal processors as known in the art. - The system 200 includes multiple states 231-233 stored in the memory 202. Typically, each state is associated with a particular context. For example, one state is associated with music context, and another state is associated with contact context. Each state is also associated with at least one function of functions 237-239. The functions 237-239 are configured to be activated based on speech inputs 233-235. Typically the functions are associated with the state in a manner similar to the association of the context with the state. For example, functions configured to select and play music are associated with the state associated with the music context. But functions configured to select and call to a particular phone number, are associated with the state associated with the contact context.
- Typically, the speech input includes an identifier of the function and a parameter of the function to be executed. For example, the speech input is "Call Joe." The identifier of the function is "Call" part of the input. Based on the identifier the function for executing telephone calls is selected from the multiple functions associated with the "telephone" state. The "Joe" part of the speech input is the parameter to the function selected based on the identifier. Accordingly, the system executes selected function using the parameter, i.e., call to a telephone number selected from a phonebook based on the name "Joe."
- The system 200 is configured to activate a function associated with the state, only when the system is transitioned into that state. For example, in order to activate a music function, the system has to be first transitioned into the state associated with the music function, and, accordingly, associated with the music context.
- Instead of having one conventional PTT button, the system 200 provides a
control panel 210, which includes multiple controls 221-223, e.g., contextual PTT controls. Each contextual PTT control can be any input control configured to be activated tangibly, such as a button, a joystick, or a touch-sensitive surface. - Each contextual PTT control 221-223 has one to one correspondence with the states 231-233. Upon activation, the contextual PTT controls generate signals 242-244. The signal can be any type of signal, e.g., a binary signal, which carries information about activated contextual PTT control.
- A
state transition module 220, upon receiving the signal, transitions the system 200 into the state associated with the signal to activate the function. For example, in one embodiment, the transition into the state is accomplished by associating 256 a data model from a set ofdata models 255 with anASR engine 250. The data model includes a vocabulary, and/or a set of predetermined commands or search terms, which allows the ASR engine to interpret the speech inputs. The ASR engine interprets the speech inputs 233-235 into inputs 261-263 expected by the functions 237-239. Accordingly, if thedata model 256 includes vocabulary of, e.g., music context, then the ASR engine can interpret only music relatedspeech input 234. Alternatively or additionally, the state transition module preselects, e.g., uploads into memory of processor 201, the functions included into the corresponding state. - The embodiments provide significant advantages over conventional systems with a single PTT button. The conventional systems require additional speech input to transition into a particular state. However, the embodiments of the invention directly transition the system into the state associated with the control based on the activation of that control.
- Hence, the system 200, in contrast with conventional systems, takes advantage of muscle memory, which is enhanced by repeated similar movements, similar to touch typing and gear shifting. Therefore, the controls are arranged so the user can activate the controls with minimal distraction from primary tasks, e.g., driving a vehicle.
- In one embodiment, each control conveys an identifier 225-227 of the context associated with the state. For example, the identifier can have a caption rendered on the control with a name of the context such as "call," or "music." Additionally or alternatively, the identifier can be a color of the control, a shape of the control, a location of the control on the device, and a combination thereof. This embodiment reduces training time usually required for a human operator to learn how to operate the embedded ASR system.
- As shown in
Figure 4 , the system 200 can be embedded in aninstrumental panel 410 of avehicle 400. Contextual PTT controls 432-433 can be arranged on asteering wheel 430. Alternatively or additionally, contextual PTT controls 425 can be place on acontrol module 420. The multiple contextual PTT controls simplify the search, and require less user interaction so that the user can concentrate on operating the vehicle. -
Figure 3 shows a block diagram of a system and method 300 according to another embodiment of the invention. In this embodiment, acontrol 310 is a multi-purpose PTT control connected via acontroller 320 to atleast functions control 310 is configured to generate a signal indicative of aparticular activation style 315 selected frommultiple activation styles 317. The activation styles include, e.g., a single click, a double click, and press and hold activation styles. - The
controller 320 activates 325 either afirst function 340 or asecond function 330 based on theparticular activation style 315. The main difference between thefunctions first function 340 can be activated based only on theactivation style 315. However, thesecond function 330 requires a speech-enabled activation, i.e., is further configured to expectspeech input 333. - This embodiment enables utilization of any conventional control as the multi-purpose PTT control. If the user activates the control in a "normal" activation style, e.g., single click, then the system activates 342 and execute 344 the first function. Otherwise, the user activates the control with a "special" activation style, e.g. double click, invoking
function 337 which expects thespeech input 333. - For example, a single click on a green call button on a telephone displays recent calls. However, a double click on the same green call button causes the system to detect speech input, e.g., a phonebook search like "John Doe", and execute a "call" function according to the speech input. In this example, the
function 340 is the function that displays the recent calls. As readily understood, thefunction 340 does not need any additional input when activated with the single click activation style. On another hand, the function that calls to a particular phone number is thefunction 330, which requires an additional input, e.g., a name of a contact from the phonebook. In this embodiment, this additional input is interpreted by the embedded ASR system based on thespeech input 333. - Similarly, "play/pause" and "shuffle" buttons on a radio can accept speech input. If the normal actuation acts as a simple toggle operation, i.e., play or pause, random playback on or off, the speech-enabled actuation detects speech input for the operation, i.e., play what, or shuffle what.
- In one embodiment, implementation of the speech-enabled activation of the
function 330 is similar to implementation of the states of the system 200. When the user instructs the system 300 to activate thesecond function 330, the system 300 is transitioned into a state associated with thesecond function 330, similar to the states 231-233. - In another embodiment, the systems 200 and 300 are combined providing multiple multi-purpose contextual PTT controls. In this embodiment, the
control panel 210 includes multiple multi-purpose PTT controls. This embodiment allows for embedding the ASR system in a device having conventional buttons turning the device into multi-purpose contextual embedded ASR system. - Although the invention has been described by way of examples of preferred embodiments, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the invention. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.
Claims (12)
- A system for activating functions including a first function and a second function, wherein the system is embedded in an apparatus, comprising:a control configured to be activated by a plurality of activation styles, wherein the control generates a signal indicative of a particular activation style from the plurality of activation styles; anda controller configured to activate either the first function or the second function based on the particular activation style, wherein the first function is configured to be executed based only on the activation style, and wherein the second function is further configured to be executed based on a speech input.
- The system of claim 1, further comprising:an automatic speech recognition (ASR) engine configured to interpret the speech input into a functional input, wherein the second function is configured to be executed based on the functional input.
- The system of claim 1, wherein the second function is selected from a plurality of functions configured to be activated based on the speech input, the system further comprising:a memory storing a plurality of states, wherein each state is associated with at least one function from the plurality of functions;a plurality of controls including the control, wherein there is one control for each state, and wherein each control is configured to generate a signal associated with the state; anda state transition module configured to transition the system to the state based on the signal to activate the function, wherein the second function is configured to be activated only when the system is in the state associated with the second function.
- The system of claim 1, wherein the speech input includes a parameter, such that the second function is executed based on the parameter.
- The system of claim 1, wherein the control is a push-to-talk button.
- The system of claim 1, wherein the apparatus is an instrumental panel of a vehicle.
- The system of claim 1, wherein the apparatus is selected from a telephone, a musical player, a navigation device, and combination thereof.
- The system of claim 1, wherein the control is a multi-purpose control, further comprising:a plurality of multi-purpose controls.
- A method for activating functions including a first function and a second function, comprising the steps of:providing a control configured to be activated by a plurality of activation styles, wherein the control generates a signal indicative of a particular activation style from the plurality of activation styles;activating either the first function or the second function based on the particular activation style, wherein the first function is configured to be executed based only on the activation style, and wherein the second function is further configured to be executed based on a speech input; andexecuting either the first function or the second function.
- The method of claim 9, further comprising:interpreting the speech input into a functional input, wherein the second function is configured to be executed based on the functional input.
- The method of claim 9, wherein the second function is selected from a plurality of functions configured to be activated based on the speech input, further comprising:providing a memory storing a plurality of states, wherein each state is associated with at least one function from the plurality of functions;providing a plurality of controls including the control, wherein there is one control for each state, and wherein each control is configured to generate a signal associated with the state; andtransitioning to the state based on the signal, wherein the second function is configured to be activated only when the system is in the state associated with the second function.
- The method of claim 9, wherein the speech input includes a parameter, such that the second function is executed based on the parameter.
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Cited By (159)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9412392B2 (en) | 2008-10-02 | 2016-08-09 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US9548050B2 (en) | 2010-01-18 | 2017-01-17 | Apple Inc. | Intelligent automated assistant |
US9582608B2 (en) | 2013-06-07 | 2017-02-28 | Apple Inc. | Unified ranking with entropy-weighted information for phrase-based semantic auto-completion |
US9620104B2 (en) | 2013-06-07 | 2017-04-11 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
US9626955B2 (en) | 2008-04-05 | 2017-04-18 | Apple Inc. | Intelligent text-to-speech conversion |
US9633674B2 (en) | 2013-06-07 | 2017-04-25 | Apple Inc. | System and method for detecting errors in interactions with a voice-based digital assistant |
US9633660B2 (en) | 2010-02-25 | 2017-04-25 | Apple Inc. | User profiling for voice input processing |
US9646609B2 (en) | 2014-09-30 | 2017-05-09 | Apple Inc. | Caching apparatus for serving phonetic pronunciations |
US9646614B2 (en) | 2000-03-16 | 2017-05-09 | Apple Inc. | Fast, language-independent method for user authentication by voice |
US9668024B2 (en) | 2014-06-30 | 2017-05-30 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US9668121B2 (en) | 2014-09-30 | 2017-05-30 | Apple Inc. | Social reminders |
US9697820B2 (en) | 2015-09-24 | 2017-07-04 | Apple Inc. | Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks |
US9721566B2 (en) | 2015-03-08 | 2017-08-01 | Apple Inc. | Competing devices responding to voice triggers |
US9798393B2 (en) | 2011-08-29 | 2017-10-24 | Apple Inc. | Text correction processing |
US9818400B2 (en) | 2014-09-11 | 2017-11-14 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US9842101B2 (en) | 2014-05-30 | 2017-12-12 | Apple Inc. | Predictive conversion of language input |
US9842105B2 (en) | 2015-04-16 | 2017-12-12 | Apple Inc. | Parsimonious continuous-space phrase representations for natural language processing |
US9865280B2 (en) | 2015-03-06 | 2018-01-09 | Apple Inc. | Structured dictation using intelligent automated assistants |
US9886953B2 (en) | 2015-03-08 | 2018-02-06 | Apple Inc. | Virtual assistant activation |
US9886432B2 (en) | 2014-09-30 | 2018-02-06 | Apple Inc. | Parsimonious handling of word inflection via categorical stem + suffix N-gram language models |
US9899019B2 (en) | 2015-03-18 | 2018-02-20 | Apple Inc. | Systems and methods for structured stem and suffix language models |
US9934775B2 (en) | 2016-05-26 | 2018-04-03 | Apple Inc. | Unit-selection text-to-speech synthesis based on predicted concatenation parameters |
US9953088B2 (en) | 2012-05-14 | 2018-04-24 | Apple Inc. | Crowd sourcing information to fulfill user requests |
US9966068B2 (en) | 2013-06-08 | 2018-05-08 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
US9971774B2 (en) | 2012-09-19 | 2018-05-15 | Apple Inc. | Voice-based media searching |
US9972304B2 (en) | 2016-06-03 | 2018-05-15 | Apple Inc. | Privacy preserving distributed evaluation framework for embedded personalized systems |
US10043516B2 (en) | 2016-09-23 | 2018-08-07 | Apple Inc. | Intelligent automated assistant |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10067938B2 (en) | 2016-06-10 | 2018-09-04 | Apple Inc. | Multilingual word prediction |
US10074360B2 (en) | 2014-09-30 | 2018-09-11 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US10079014B2 (en) | 2012-06-08 | 2018-09-18 | Apple Inc. | Name recognition system |
US10083688B2 (en) | 2015-05-27 | 2018-09-25 | Apple Inc. | Device voice control for selecting a displayed affordance |
US10083690B2 (en) | 2014-05-30 | 2018-09-25 | Apple Inc. | Better resolution when referencing to concepts |
US10089072B2 (en) | 2016-06-11 | 2018-10-02 | Apple Inc. | Intelligent device arbitration and control |
US10101822B2 (en) | 2015-06-05 | 2018-10-16 | Apple Inc. | Language input correction |
US10102359B2 (en) | 2011-03-21 | 2018-10-16 | Apple Inc. | Device access using voice authentication |
US10108612B2 (en) | 2008-07-31 | 2018-10-23 | Apple Inc. | Mobile device having human language translation capability with positional feedback |
US10127220B2 (en) | 2015-06-04 | 2018-11-13 | Apple Inc. | Language identification from short strings |
US10127911B2 (en) | 2014-09-30 | 2018-11-13 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US10169329B2 (en) | 2014-05-30 | 2019-01-01 | Apple Inc. | Exemplar-based natural language processing |
US10176167B2 (en) | 2013-06-09 | 2019-01-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
US10186254B2 (en) | 2015-06-07 | 2019-01-22 | Apple Inc. | Context-based endpoint detection |
US10185542B2 (en) | 2013-06-09 | 2019-01-22 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US10192552B2 (en) | 2016-06-10 | 2019-01-29 | Apple Inc. | Digital assistant providing whispered speech |
US10223066B2 (en) | 2015-12-23 | 2019-03-05 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10249300B2 (en) | 2016-06-06 | 2019-04-02 | Apple Inc. | Intelligent list reading |
US10255907B2 (en) | 2015-06-07 | 2019-04-09 | Apple Inc. | Automatic accent detection using acoustic models |
US10269345B2 (en) | 2016-06-11 | 2019-04-23 | Apple Inc. | Intelligent task discovery |
US10283110B2 (en) | 2009-07-02 | 2019-05-07 | Apple Inc. | Methods and apparatuses for automatic speech recognition |
US10297253B2 (en) | 2016-06-11 | 2019-05-21 | Apple Inc. | Application integration with a digital assistant |
US10303715B2 (en) | 2017-05-16 | 2019-05-28 | Apple Inc. | Intelligent automated assistant for media exploration |
US10311144B2 (en) | 2017-05-16 | 2019-06-04 | Apple Inc. | Emoji word sense disambiguation |
US10318871B2 (en) | 2005-09-08 | 2019-06-11 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US10332518B2 (en) | 2017-05-09 | 2019-06-25 | Apple Inc. | User interface for correcting recognition errors |
US10356243B2 (en) | 2015-06-05 | 2019-07-16 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US10354011B2 (en) | 2016-06-09 | 2019-07-16 | Apple Inc. | Intelligent automated assistant in a home environment |
US10366158B2 (en) | 2015-09-29 | 2019-07-30 | Apple Inc. | Efficient word encoding for recurrent neural network language models |
US10381016B2 (en) | 2008-01-03 | 2019-08-13 | Apple Inc. | Methods and apparatus for altering audio output signals |
US10395654B2 (en) | 2017-05-11 | 2019-08-27 | Apple Inc. | Text normalization based on a data-driven learning network |
US10403283B1 (en) | 2018-06-01 | 2019-09-03 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US10403278B2 (en) | 2017-05-16 | 2019-09-03 | Apple Inc. | Methods and systems for phonetic matching in digital assistant services |
US10410637B2 (en) | 2017-05-12 | 2019-09-10 | Apple Inc. | User-specific acoustic models |
US10417266B2 (en) | 2017-05-09 | 2019-09-17 | Apple Inc. | Context-aware ranking of intelligent response suggestions |
US10446143B2 (en) | 2016-03-14 | 2019-10-15 | Apple Inc. | Identification of voice inputs providing credentials |
US10445429B2 (en) | 2017-09-21 | 2019-10-15 | Apple Inc. | Natural language understanding using vocabularies with compressed serialized tries |
US10474753B2 (en) | 2016-09-07 | 2019-11-12 | Apple Inc. | Language identification using recurrent neural networks |
US10482874B2 (en) | 2017-05-15 | 2019-11-19 | Apple Inc. | Hierarchical belief states for digital assistants |
US10490187B2 (en) | 2016-06-10 | 2019-11-26 | Apple Inc. | Digital assistant providing automated status report |
US10496705B1 (en) | 2018-06-03 | 2019-12-03 | Apple Inc. | Accelerated task performance |
US10496753B2 (en) | 2010-01-18 | 2019-12-03 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
US10497365B2 (en) | 2014-05-30 | 2019-12-03 | Apple Inc. | Multi-command single utterance input method |
US10509862B2 (en) | 2016-06-10 | 2019-12-17 | Apple Inc. | Dynamic phrase expansion of language input |
US10521466B2 (en) | 2016-06-11 | 2019-12-31 | Apple Inc. | Data driven natural language event detection and classification |
US10553209B2 (en) | 2010-01-18 | 2020-02-04 | Apple Inc. | Systems and methods for hands-free notification summaries |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
US10592604B2 (en) | 2018-03-12 | 2020-03-17 | Apple Inc. | Inverse text normalization for automatic speech recognition |
US10636424B2 (en) | 2017-11-30 | 2020-04-28 | Apple Inc. | Multi-turn canned dialog |
US10657328B2 (en) | 2017-06-02 | 2020-05-19 | Apple Inc. | Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
US10679605B2 (en) | 2010-01-18 | 2020-06-09 | Apple Inc. | Hands-free list-reading by intelligent automated assistant |
US10684703B2 (en) | 2018-06-01 | 2020-06-16 | Apple Inc. | Attention aware virtual assistant dismissal |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US10699717B2 (en) | 2014-05-30 | 2020-06-30 | Apple Inc. | Intelligent assistant for home automation |
US10705794B2 (en) | 2010-01-18 | 2020-07-07 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
US10714117B2 (en) | 2013-02-07 | 2020-07-14 | Apple Inc. | Voice trigger for a digital assistant |
US10726832B2 (en) | 2017-05-11 | 2020-07-28 | Apple Inc. | Maintaining privacy of personal information |
US10733375B2 (en) | 2018-01-31 | 2020-08-04 | Apple Inc. | Knowledge-based framework for improving natural language understanding |
US10733993B2 (en) | 2016-06-10 | 2020-08-04 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10733982B2 (en) | 2018-01-08 | 2020-08-04 | Apple Inc. | Multi-directional dialog |
US10741185B2 (en) | 2010-01-18 | 2020-08-11 | Apple Inc. | Intelligent automated assistant |
US10748546B2 (en) | 2017-05-16 | 2020-08-18 | Apple Inc. | Digital assistant services based on device capabilities |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US10755703B2 (en) | 2017-05-11 | 2020-08-25 | Apple Inc. | Offline personal assistant |
US10755051B2 (en) | 2017-09-29 | 2020-08-25 | Apple Inc. | Rule-based natural language processing |
US10789041B2 (en) | 2014-09-12 | 2020-09-29 | Apple Inc. | Dynamic thresholds for always listening speech trigger |
US10791176B2 (en) | 2017-05-12 | 2020-09-29 | Apple Inc. | Synchronization and task delegation of a digital assistant |
US10789945B2 (en) | 2017-05-12 | 2020-09-29 | Apple Inc. | Low-latency intelligent automated assistant |
US10789959B2 (en) | 2018-03-02 | 2020-09-29 | Apple Inc. | Training speaker recognition models for digital assistants |
US10795541B2 (en) | 2009-06-05 | 2020-10-06 | Apple Inc. | Intelligent organization of tasks items |
US10810274B2 (en) | 2017-05-15 | 2020-10-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
US10818288B2 (en) | 2018-03-26 | 2020-10-27 | Apple Inc. | Natural assistant interaction |
US10839159B2 (en) | 2018-09-28 | 2020-11-17 | Apple Inc. | Named entity normalization in a spoken dialog system |
US10892996B2 (en) | 2018-06-01 | 2021-01-12 | Apple Inc. | Variable latency device coordination |
US10909331B2 (en) | 2018-03-30 | 2021-02-02 | Apple Inc. | Implicit identification of translation payload with neural machine translation |
US10928918B2 (en) | 2018-05-07 | 2021-02-23 | Apple Inc. | Raise to speak |
US10984780B2 (en) | 2018-05-21 | 2021-04-20 | Apple Inc. | Global semantic word embeddings using bi-directional recurrent neural networks |
US11010550B2 (en) | 2015-09-29 | 2021-05-18 | Apple Inc. | Unified language modeling framework for word prediction, auto-completion and auto-correction |
US11010561B2 (en) | 2018-09-27 | 2021-05-18 | Apple Inc. | Sentiment prediction from textual data |
US11010127B2 (en) | 2015-06-29 | 2021-05-18 | Apple Inc. | Virtual assistant for media playback |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US11023513B2 (en) | 2007-12-20 | 2021-06-01 | Apple Inc. | Method and apparatus for searching using an active ontology |
US11069336B2 (en) | 2012-03-02 | 2021-07-20 | Apple Inc. | Systems and methods for name pronunciation |
US11070949B2 (en) | 2015-05-27 | 2021-07-20 | Apple Inc. | Systems and methods for proactively identifying and surfacing relevant content on an electronic device with a touch-sensitive display |
US11080012B2 (en) | 2009-06-05 | 2021-08-03 | Apple Inc. | Interface for a virtual digital assistant |
US11120372B2 (en) | 2011-06-03 | 2021-09-14 | Apple Inc. | Performing actions associated with task items that represent tasks to perform |
US11133008B2 (en) | 2014-05-30 | 2021-09-28 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US11140099B2 (en) | 2019-05-21 | 2021-10-05 | Apple Inc. | Providing message response suggestions |
US11145294B2 (en) | 2018-05-07 | 2021-10-12 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US11170166B2 (en) | 2018-09-28 | 2021-11-09 | Apple Inc. | Neural typographical error modeling via generative adversarial networks |
US11204787B2 (en) | 2017-01-09 | 2021-12-21 | Apple Inc. | Application integration with a digital assistant |
US11217251B2 (en) | 2019-05-06 | 2022-01-04 | Apple Inc. | Spoken notifications |
US11227589B2 (en) | 2016-06-06 | 2022-01-18 | Apple Inc. | Intelligent list reading |
US11231904B2 (en) | 2015-03-06 | 2022-01-25 | Apple Inc. | Reducing response latency of intelligent automated assistants |
US11237797B2 (en) | 2019-05-31 | 2022-02-01 | Apple Inc. | User activity shortcut suggestions |
US11269678B2 (en) | 2012-05-15 | 2022-03-08 | Apple Inc. | Systems and methods for integrating third party services with a digital assistant |
US11281993B2 (en) | 2016-12-05 | 2022-03-22 | Apple Inc. | Model and ensemble compression for metric learning |
US11289073B2 (en) | 2019-05-31 | 2022-03-29 | Apple Inc. | Device text to speech |
US11301477B2 (en) | 2017-05-12 | 2022-04-12 | Apple Inc. | Feedback analysis of a digital assistant |
US11307752B2 (en) | 2019-05-06 | 2022-04-19 | Apple Inc. | User configurable task triggers |
US11314370B2 (en) | 2013-12-06 | 2022-04-26 | Apple Inc. | Method for extracting salient dialog usage from live data |
US11348573B2 (en) | 2019-03-18 | 2022-05-31 | Apple Inc. | Multimodality in digital assistant systems |
US11350253B2 (en) | 2011-06-03 | 2022-05-31 | Apple Inc. | Active transport based notifications |
US11360641B2 (en) | 2019-06-01 | 2022-06-14 | Apple Inc. | Increasing the relevance of new available information |
US11386266B2 (en) | 2018-06-01 | 2022-07-12 | Apple Inc. | Text correction |
US11388291B2 (en) | 2013-03-14 | 2022-07-12 | Apple Inc. | System and method for processing voicemail |
US11423908B2 (en) | 2019-05-06 | 2022-08-23 | Apple Inc. | Interpreting spoken requests |
US11462215B2 (en) | 2018-09-28 | 2022-10-04 | Apple Inc. | Multi-modal inputs for voice commands |
US11468282B2 (en) | 2015-05-15 | 2022-10-11 | Apple Inc. | Virtual assistant in a communication session |
US11475898B2 (en) | 2018-10-26 | 2022-10-18 | Apple Inc. | Low-latency multi-speaker speech recognition |
US11475884B2 (en) | 2019-05-06 | 2022-10-18 | Apple Inc. | Reducing digital assistant latency when a language is incorrectly determined |
US11488406B2 (en) | 2019-09-25 | 2022-11-01 | Apple Inc. | Text detection using global geometry estimators |
US11496600B2 (en) | 2019-05-31 | 2022-11-08 | Apple Inc. | Remote execution of machine-learned models |
US11495218B2 (en) | 2018-06-01 | 2022-11-08 | Apple Inc. | Virtual assistant operation in multi-device environments |
US11532306B2 (en) | 2017-05-16 | 2022-12-20 | Apple Inc. | Detecting a trigger of a digital assistant |
US11587559B2 (en) | 2015-09-30 | 2023-02-21 | Apple Inc. | Intelligent device identification |
US11638059B2 (en) | 2019-01-04 | 2023-04-25 | Apple Inc. | Content playback on multiple devices |
US11657813B2 (en) | 2019-05-31 | 2023-05-23 | Apple Inc. | Voice identification in digital assistant systems |
US11671920B2 (en) | 2007-04-03 | 2023-06-06 | Apple Inc. | Method and system for operating a multifunction portable electronic device using voice-activation |
US11755276B2 (en) | 2020-05-12 | 2023-09-12 | Apple Inc. | Reducing description length based on confidence |
US11765209B2 (en) | 2020-05-11 | 2023-09-19 | Apple Inc. | Digital assistant hardware abstraction |
US11798547B2 (en) | 2013-03-15 | 2023-10-24 | Apple Inc. | Voice activated device for use with a voice-based digital assistant |
US11809483B2 (en) | 2015-09-08 | 2023-11-07 | Apple Inc. | Intelligent automated assistant for media search and playback |
US11853536B2 (en) | 2015-09-08 | 2023-12-26 | Apple Inc. | Intelligent automated assistant in a media environment |
US11886805B2 (en) | 2015-11-09 | 2024-01-30 | Apple Inc. | Unconventional virtual assistant interactions |
US12010262B2 (en) | 2013-08-06 | 2024-06-11 | Apple Inc. | Auto-activating smart responses based on activities from remote devices |
US12223282B2 (en) | 2016-06-09 | 2025-02-11 | Apple Inc. | Intelligent automated assistant in a home environment |
US12277954B2 (en) | 2024-04-16 | 2025-04-15 | Apple Inc. | Voice trigger for a digital assistant |
Families Citing this family (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110060588A1 (en) * | 2009-09-10 | 2011-03-10 | Weinberg Garrett L | Method and System for Automatic Speech Recognition with Multiple Contexts |
CN108337380B (en) * | 2011-09-30 | 2022-08-19 | 苹果公司 | Automatically adjusting user interface for hands-free interaction |
WO2013069060A1 (en) * | 2011-11-10 | 2013-05-16 | 三菱電機株式会社 | Navigation device and method |
KR101987255B1 (en) * | 2012-08-20 | 2019-06-11 | 엘지이노텍 주식회사 | Speech recognition device and speech recognition method |
US9026983B2 (en) * | 2013-03-15 | 2015-05-05 | Ittiam Systems (P) Ltd. | Flexible and scalable software system architecture for implementing multimedia applications |
US10180785B2 (en) * | 2014-05-07 | 2019-01-15 | Livio, Inc. | Global and contextual vehicle computing system controls |
US10049666B2 (en) | 2016-01-06 | 2018-08-14 | Google Llc | Voice recognition system |
DE102020105042A1 (en) | 2020-02-26 | 2021-08-26 | Audi Aktiengesellschaft | Method for setting an operating device |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4989253A (en) | 1988-04-15 | 1991-01-29 | The Montefiore Hospital Association Of Western Pennsylvania | Voice activated microscope |
JPH0934488A (en) * | 1995-07-18 | 1997-02-07 | Mazda Motor Corp | Voice operating device for car on-board apparatus |
US5970457A (en) | 1995-10-25 | 1999-10-19 | Johns Hopkins University | Voice command and control medical care system |
US7542966B2 (en) | 2002-04-25 | 2009-06-02 | Mitsubishi Electric Research Laboratories, Inc. | Method and system for retrieving documents with spoken queries |
Family Cites Families (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7219123B1 (en) * | 1999-10-08 | 2007-05-15 | At Road, Inc. | Portable browser device with adaptive personalization capability |
US6230138B1 (en) | 2000-06-28 | 2001-05-08 | Visteon Global Technologies, Inc. | Method and apparatus for controlling multiple speech engines in an in-vehicle speech recognition system |
JP2002108390A (en) * | 2000-09-27 | 2002-04-10 | Sharp Corp | Speech recognition system and computer-readable recording medium |
US20030069733A1 (en) * | 2001-10-02 | 2003-04-10 | Ryan Chang | Voice control method utilizing a single-key pushbutton to control voice commands and a device thereof |
JP2003195890A (en) * | 2001-12-25 | 2003-07-09 | Nippon Seiki Co Ltd | Speech operating device |
JP2003329477A (en) * | 2002-05-15 | 2003-11-19 | Pioneer Electronic Corp | Navigation device and interactive information providing program |
US20050009604A1 (en) * | 2003-07-11 | 2005-01-13 | Hsien-Ta Huang | Monotone voice activation device |
JP4498906B2 (en) * | 2004-12-03 | 2010-07-07 | 三菱電機株式会社 | Voice recognition device |
CN1805475A (en) * | 2005-01-10 | 2006-07-19 | 陈修志 | Basal keystroke sound controlled onboard mobile telephone device |
CN2862265Y (en) | 2005-10-31 | 2007-01-24 | 陈修志 | Audio control MP3 player |
US7996228B2 (en) | 2005-12-22 | 2011-08-09 | Microsoft Corporation | Voice initiated network operations |
CN200966481Y (en) * | 2006-03-06 | 2007-10-31 | 蒿慧君 | Intelligent bed controlled by voice |
US8078188B2 (en) * | 2007-01-16 | 2011-12-13 | Qualcomm Incorporated | User selectable audio mixing |
CN201126359Y (en) * | 2007-11-29 | 2008-10-01 | 厉天福 | Vehicle mounted multimedia navigation device |
US20110060588A1 (en) * | 2009-09-10 | 2011-03-10 | Weinberg Garrett L | Method and System for Automatic Speech Recognition with Multiple Contexts |
-
2009
- 2009-09-10 US US12/557,010 patent/US8788267B2/en not_active Expired - Fee Related
-
2010
- 2010-06-11 JP JP2010133546A patent/JP2011059659A/en active Pending
- 2010-07-09 EP EP10007096A patent/EP2309491A1/en not_active Withdrawn
- 2010-09-10 CN CN2010102810804A patent/CN102024013A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4989253A (en) | 1988-04-15 | 1991-01-29 | The Montefiore Hospital Association Of Western Pennsylvania | Voice activated microscope |
JPH0934488A (en) * | 1995-07-18 | 1997-02-07 | Mazda Motor Corp | Voice operating device for car on-board apparatus |
US5970457A (en) | 1995-10-25 | 1999-10-19 | Johns Hopkins University | Voice command and control medical care system |
US7542966B2 (en) | 2002-04-25 | 2009-06-02 | Mitsubishi Electric Research Laboratories, Inc. | Method and system for retrieving documents with spoken queries |
Non-Patent Citations (3)
Title |
---|
O. PALINKO ET AL: "Steering wheel sensor as a push-to-talk solution", 4TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENVIRONMENTS (IE 08), 21 July 2008 (2008-07-21), Seattle, WA, pages 1 - 4, XP055277797, ISBN: 978-0-86341-894-5, DOI: 10.1049/cp:20081094 * |
PERENSON M. J.: "Apple iPhone 3GS Reviewed", 23 June 2009 (2009-06-23), XP002610086, Retrieved from the Internet <URL:http://www.pcworld.com/reviews/product/116744/review/32gb_iphone_3gs.html> [retrieved on 20101117] * |
WEINBERG G: "Contextual Push-to-Talk: A New Technique for Reducing Voice Dialog Duration", PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON HUMAN-COMPUTER INTERACTION WITH MOBILE DEVICES AND SERVICES, 15 September 2009 (2009-09-15), XP002610087, DOI: 10.1145/1613858.1613960 * |
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---|---|---|---|---|
US9646614B2 (en) | 2000-03-16 | 2017-05-09 | Apple Inc. | Fast, language-independent method for user authentication by voice |
US11928604B2 (en) | 2005-09-08 | 2024-03-12 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US10318871B2 (en) | 2005-09-08 | 2019-06-11 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US11671920B2 (en) | 2007-04-03 | 2023-06-06 | Apple Inc. | Method and system for operating a multifunction portable electronic device using voice-activation |
US11023513B2 (en) | 2007-12-20 | 2021-06-01 | Apple Inc. | Method and apparatus for searching using an active ontology |
US10381016B2 (en) | 2008-01-03 | 2019-08-13 | Apple Inc. | Methods and apparatus for altering audio output signals |
US9626955B2 (en) | 2008-04-05 | 2017-04-18 | Apple Inc. | Intelligent text-to-speech conversion |
US9865248B2 (en) | 2008-04-05 | 2018-01-09 | Apple Inc. | Intelligent text-to-speech conversion |
US10108612B2 (en) | 2008-07-31 | 2018-10-23 | Apple Inc. | Mobile device having human language translation capability with positional feedback |
US9412392B2 (en) | 2008-10-02 | 2016-08-09 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US11348582B2 (en) | 2008-10-02 | 2022-05-31 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US10643611B2 (en) | 2008-10-02 | 2020-05-05 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US10795541B2 (en) | 2009-06-05 | 2020-10-06 | Apple Inc. | Intelligent organization of tasks items |
US11080012B2 (en) | 2009-06-05 | 2021-08-03 | Apple Inc. | Interface for a virtual digital assistant |
US10283110B2 (en) | 2009-07-02 | 2019-05-07 | Apple Inc. | Methods and apparatuses for automatic speech recognition |
US10705794B2 (en) | 2010-01-18 | 2020-07-07 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
US12087308B2 (en) | 2010-01-18 | 2024-09-10 | Apple Inc. | Intelligent automated assistant |
US9548050B2 (en) | 2010-01-18 | 2017-01-17 | Apple Inc. | Intelligent automated assistant |
US10741185B2 (en) | 2010-01-18 | 2020-08-11 | Apple Inc. | Intelligent automated assistant |
US10496753B2 (en) | 2010-01-18 | 2019-12-03 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
US10679605B2 (en) | 2010-01-18 | 2020-06-09 | Apple Inc. | Hands-free list-reading by intelligent automated assistant |
US10553209B2 (en) | 2010-01-18 | 2020-02-04 | Apple Inc. | Systems and methods for hands-free notification summaries |
US11423886B2 (en) | 2010-01-18 | 2022-08-23 | Apple Inc. | Task flow identification based on user intent |
US10706841B2 (en) | 2010-01-18 | 2020-07-07 | Apple Inc. | Task flow identification based on user intent |
US10049675B2 (en) | 2010-02-25 | 2018-08-14 | Apple Inc. | User profiling for voice input processing |
US10692504B2 (en) | 2010-02-25 | 2020-06-23 | Apple Inc. | User profiling for voice input processing |
US9633660B2 (en) | 2010-02-25 | 2017-04-25 | Apple Inc. | User profiling for voice input processing |
US10102359B2 (en) | 2011-03-21 | 2018-10-16 | Apple Inc. | Device access using voice authentication |
US10417405B2 (en) | 2011-03-21 | 2019-09-17 | Apple Inc. | Device access using voice authentication |
US11350253B2 (en) | 2011-06-03 | 2022-05-31 | Apple Inc. | Active transport based notifications |
US11120372B2 (en) | 2011-06-03 | 2021-09-14 | Apple Inc. | Performing actions associated with task items that represent tasks to perform |
US9798393B2 (en) | 2011-08-29 | 2017-10-24 | Apple Inc. | Text correction processing |
US11069336B2 (en) | 2012-03-02 | 2021-07-20 | Apple Inc. | Systems and methods for name pronunciation |
US9953088B2 (en) | 2012-05-14 | 2018-04-24 | Apple Inc. | Crowd sourcing information to fulfill user requests |
US11321116B2 (en) | 2012-05-15 | 2022-05-03 | Apple Inc. | Systems and methods for integrating third party services with a digital assistant |
US11269678B2 (en) | 2012-05-15 | 2022-03-08 | Apple Inc. | Systems and methods for integrating third party services with a digital assistant |
US10079014B2 (en) | 2012-06-08 | 2018-09-18 | Apple Inc. | Name recognition system |
US9971774B2 (en) | 2012-09-19 | 2018-05-15 | Apple Inc. | Voice-based media searching |
US11636869B2 (en) | 2013-02-07 | 2023-04-25 | Apple Inc. | Voice trigger for a digital assistant |
US10978090B2 (en) | 2013-02-07 | 2021-04-13 | Apple Inc. | Voice trigger for a digital assistant |
US10714117B2 (en) | 2013-02-07 | 2020-07-14 | Apple Inc. | Voice trigger for a digital assistant |
US11388291B2 (en) | 2013-03-14 | 2022-07-12 | Apple Inc. | System and method for processing voicemail |
US11798547B2 (en) | 2013-03-15 | 2023-10-24 | Apple Inc. | Voice activated device for use with a voice-based digital assistant |
US9633674B2 (en) | 2013-06-07 | 2017-04-25 | Apple Inc. | System and method for detecting errors in interactions with a voice-based digital assistant |
US9582608B2 (en) | 2013-06-07 | 2017-02-28 | Apple Inc. | Unified ranking with entropy-weighted information for phrase-based semantic auto-completion |
US9966060B2 (en) | 2013-06-07 | 2018-05-08 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
US9620104B2 (en) | 2013-06-07 | 2017-04-11 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
US10657961B2 (en) | 2013-06-08 | 2020-05-19 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
US9966068B2 (en) | 2013-06-08 | 2018-05-08 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
US10185542B2 (en) | 2013-06-09 | 2019-01-22 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US11048473B2 (en) | 2013-06-09 | 2021-06-29 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US10769385B2 (en) | 2013-06-09 | 2020-09-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
US12073147B2 (en) | 2013-06-09 | 2024-08-27 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US10176167B2 (en) | 2013-06-09 | 2019-01-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
US11727219B2 (en) | 2013-06-09 | 2023-08-15 | Apple Inc. | System and method for inferring user intent from speech inputs |
US12010262B2 (en) | 2013-08-06 | 2024-06-11 | Apple Inc. | Auto-activating smart responses based on activities from remote devices |
US11314370B2 (en) | 2013-12-06 | 2022-04-26 | Apple Inc. | Method for extracting salient dialog usage from live data |
US11699448B2 (en) | 2014-05-30 | 2023-07-11 | Apple Inc. | Intelligent assistant for home automation |
US10417344B2 (en) | 2014-05-30 | 2019-09-17 | Apple Inc. | Exemplar-based natural language processing |
US11670289B2 (en) | 2014-05-30 | 2023-06-06 | Apple Inc. | Multi-command single utterance input method |
US10699717B2 (en) | 2014-05-30 | 2020-06-30 | Apple Inc. | Intelligent assistant for home automation |
US10878809B2 (en) | 2014-05-30 | 2020-12-29 | Apple Inc. | Multi-command single utterance input method |
US10657966B2 (en) | 2014-05-30 | 2020-05-19 | Apple Inc. | Better resolution when referencing to concepts |
US9842101B2 (en) | 2014-05-30 | 2017-12-12 | Apple Inc. | Predictive conversion of language input |
US10714095B2 (en) | 2014-05-30 | 2020-07-14 | Apple Inc. | Intelligent assistant for home automation |
US11133008B2 (en) | 2014-05-30 | 2021-09-28 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US11257504B2 (en) | 2014-05-30 | 2022-02-22 | Apple Inc. | Intelligent assistant for home automation |
US11810562B2 (en) | 2014-05-30 | 2023-11-07 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US10083690B2 (en) | 2014-05-30 | 2018-09-25 | Apple Inc. | Better resolution when referencing to concepts |
US10497365B2 (en) | 2014-05-30 | 2019-12-03 | Apple Inc. | Multi-command single utterance input method |
US10169329B2 (en) | 2014-05-30 | 2019-01-01 | Apple Inc. | Exemplar-based natural language processing |
US11516537B2 (en) | 2014-06-30 | 2022-11-29 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US9668024B2 (en) | 2014-06-30 | 2017-05-30 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US10904611B2 (en) | 2014-06-30 | 2021-01-26 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US10431204B2 (en) | 2014-09-11 | 2019-10-01 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US9818400B2 (en) | 2014-09-11 | 2017-11-14 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US10789041B2 (en) | 2014-09-12 | 2020-09-29 | Apple Inc. | Dynamic thresholds for always listening speech trigger |
US9986419B2 (en) | 2014-09-30 | 2018-05-29 | Apple Inc. | Social reminders |
US10074360B2 (en) | 2014-09-30 | 2018-09-11 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US10438595B2 (en) | 2014-09-30 | 2019-10-08 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US9886432B2 (en) | 2014-09-30 | 2018-02-06 | Apple Inc. | Parsimonious handling of word inflection via categorical stem + suffix N-gram language models |
US9646609B2 (en) | 2014-09-30 | 2017-05-09 | Apple Inc. | Caching apparatus for serving phonetic pronunciations |
US9668121B2 (en) | 2014-09-30 | 2017-05-30 | Apple Inc. | Social reminders |
US10127911B2 (en) | 2014-09-30 | 2018-11-13 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US10453443B2 (en) | 2014-09-30 | 2019-10-22 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US10390213B2 (en) | 2014-09-30 | 2019-08-20 | Apple Inc. | Social reminders |
US11231904B2 (en) | 2015-03-06 | 2022-01-25 | Apple Inc. | Reducing response latency of intelligent automated assistants |
US9865280B2 (en) | 2015-03-06 | 2018-01-09 | Apple Inc. | Structured dictation using intelligent automated assistants |
US11087759B2 (en) | 2015-03-08 | 2021-08-10 | Apple Inc. | Virtual assistant activation |
US10311871B2 (en) | 2015-03-08 | 2019-06-04 | Apple Inc. | Competing devices responding to voice triggers |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US10529332B2 (en) | 2015-03-08 | 2020-01-07 | Apple Inc. | Virtual assistant activation |
US11842734B2 (en) | 2015-03-08 | 2023-12-12 | Apple Inc. | Virtual assistant activation |
US9721566B2 (en) | 2015-03-08 | 2017-08-01 | Apple Inc. | Competing devices responding to voice triggers |
US9886953B2 (en) | 2015-03-08 | 2018-02-06 | Apple Inc. | Virtual assistant activation |
US10930282B2 (en) | 2015-03-08 | 2021-02-23 | Apple Inc. | Competing devices responding to voice triggers |
US9899019B2 (en) | 2015-03-18 | 2018-02-20 | Apple Inc. | Systems and methods for structured stem and suffix language models |
US9842105B2 (en) | 2015-04-16 | 2017-12-12 | Apple Inc. | Parsimonious continuous-space phrase representations for natural language processing |
US11468282B2 (en) | 2015-05-15 | 2022-10-11 | Apple Inc. | Virtual assistant in a communication session |
US11070949B2 (en) | 2015-05-27 | 2021-07-20 | Apple Inc. | Systems and methods for proactively identifying and surfacing relevant content on an electronic device with a touch-sensitive display |
US10083688B2 (en) | 2015-05-27 | 2018-09-25 | Apple Inc. | Device voice control for selecting a displayed affordance |
US11127397B2 (en) | 2015-05-27 | 2021-09-21 | Apple Inc. | Device voice control |
US10127220B2 (en) | 2015-06-04 | 2018-11-13 | Apple Inc. | Language identification from short strings |
US10356243B2 (en) | 2015-06-05 | 2019-07-16 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US10681212B2 (en) | 2015-06-05 | 2020-06-09 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US10101822B2 (en) | 2015-06-05 | 2018-10-16 | Apple Inc. | Language input correction |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US10186254B2 (en) | 2015-06-07 | 2019-01-22 | Apple Inc. | Context-based endpoint detection |
US10255907B2 (en) | 2015-06-07 | 2019-04-09 | Apple Inc. | Automatic accent detection using acoustic models |
US11010127B2 (en) | 2015-06-29 | 2021-05-18 | Apple Inc. | Virtual assistant for media playback |
US11947873B2 (en) | 2015-06-29 | 2024-04-02 | Apple Inc. | Virtual assistant for media playback |
US11550542B2 (en) | 2015-09-08 | 2023-01-10 | Apple Inc. | Zero latency digital assistant |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US11853536B2 (en) | 2015-09-08 | 2023-12-26 | Apple Inc. | Intelligent automated assistant in a media environment |
US11126400B2 (en) | 2015-09-08 | 2021-09-21 | Apple Inc. | Zero latency digital assistant |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
US11809483B2 (en) | 2015-09-08 | 2023-11-07 | Apple Inc. | Intelligent automated assistant for media search and playback |
US11500672B2 (en) | 2015-09-08 | 2022-11-15 | Apple Inc. | Distributed personal assistant |
US9697820B2 (en) | 2015-09-24 | 2017-07-04 | Apple Inc. | Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks |
US11010550B2 (en) | 2015-09-29 | 2021-05-18 | Apple Inc. | Unified language modeling framework for word prediction, auto-completion and auto-correction |
US10366158B2 (en) | 2015-09-29 | 2019-07-30 | Apple Inc. | Efficient word encoding for recurrent neural network language models |
US11587559B2 (en) | 2015-09-30 | 2023-02-21 | Apple Inc. | Intelligent device identification |
US11526368B2 (en) | 2015-11-06 | 2022-12-13 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US11886805B2 (en) | 2015-11-09 | 2024-01-30 | Apple Inc. | Unconventional virtual assistant interactions |
US10354652B2 (en) | 2015-12-02 | 2019-07-16 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10223066B2 (en) | 2015-12-23 | 2019-03-05 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10942703B2 (en) | 2015-12-23 | 2021-03-09 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10446143B2 (en) | 2016-03-14 | 2019-10-15 | Apple Inc. | Identification of voice inputs providing credentials |
US9934775B2 (en) | 2016-05-26 | 2018-04-03 | Apple Inc. | Unit-selection text-to-speech synthesis based on predicted concatenation parameters |
US9972304B2 (en) | 2016-06-03 | 2018-05-15 | Apple Inc. | Privacy preserving distributed evaluation framework for embedded personalized systems |
US10249300B2 (en) | 2016-06-06 | 2019-04-02 | Apple Inc. | Intelligent list reading |
US11227589B2 (en) | 2016-06-06 | 2022-01-18 | Apple Inc. | Intelligent list reading |
US11069347B2 (en) | 2016-06-08 | 2021-07-20 | Apple Inc. | Intelligent automated assistant for media exploration |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
US10354011B2 (en) | 2016-06-09 | 2019-07-16 | Apple Inc. | Intelligent automated assistant in a home environment |
US12223282B2 (en) | 2016-06-09 | 2025-02-11 | Apple Inc. | Intelligent automated assistant in a home environment |
US11657820B2 (en) | 2016-06-10 | 2023-05-23 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10192552B2 (en) | 2016-06-10 | 2019-01-29 | Apple Inc. | Digital assistant providing whispered speech |
US10509862B2 (en) | 2016-06-10 | 2019-12-17 | Apple Inc. | Dynamic phrase expansion of language input |
US11037565B2 (en) | 2016-06-10 | 2021-06-15 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10067938B2 (en) | 2016-06-10 | 2018-09-04 | Apple Inc. | Multilingual word prediction |
US10733993B2 (en) | 2016-06-10 | 2020-08-04 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10490187B2 (en) | 2016-06-10 | 2019-11-26 | Apple Inc. | Digital assistant providing automated status report |
US10297253B2 (en) | 2016-06-11 | 2019-05-21 | Apple Inc. | Application integration with a digital assistant |
US11749275B2 (en) | 2016-06-11 | 2023-09-05 | Apple Inc. | Application integration with a digital assistant |
US10580409B2 (en) | 2016-06-11 | 2020-03-03 | Apple Inc. | Application integration with a digital assistant |
US11809783B2 (en) | 2016-06-11 | 2023-11-07 | Apple Inc. | Intelligent device arbitration and control |
US10089072B2 (en) | 2016-06-11 | 2018-10-02 | Apple Inc. | Intelligent device arbitration and control |
US10521466B2 (en) | 2016-06-11 | 2019-12-31 | Apple Inc. | Data driven natural language event detection and classification |
US10942702B2 (en) | 2016-06-11 | 2021-03-09 | Apple Inc. | Intelligent device arbitration and control |
US11152002B2 (en) | 2016-06-11 | 2021-10-19 | Apple Inc. | Application integration with a digital assistant |
US10269345B2 (en) | 2016-06-11 | 2019-04-23 | Apple Inc. | Intelligent task discovery |
US10474753B2 (en) | 2016-09-07 | 2019-11-12 | Apple Inc. | Language identification using recurrent neural networks |
US10043516B2 (en) | 2016-09-23 | 2018-08-07 | Apple Inc. | Intelligent automated assistant |
US10553215B2 (en) | 2016-09-23 | 2020-02-04 | Apple Inc. | Intelligent automated assistant |
US11281993B2 (en) | 2016-12-05 | 2022-03-22 | Apple Inc. | Model and ensemble compression for metric learning |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
US11204787B2 (en) | 2017-01-09 | 2021-12-21 | Apple Inc. | Application integration with a digital assistant |
US11656884B2 (en) | 2017-01-09 | 2023-05-23 | Apple Inc. | Application integration with a digital assistant |
US10417266B2 (en) | 2017-05-09 | 2019-09-17 | Apple Inc. | Context-aware ranking of intelligent response suggestions |
US10332518B2 (en) | 2017-05-09 | 2019-06-25 | Apple Inc. | User interface for correcting recognition errors |
US10741181B2 (en) | 2017-05-09 | 2020-08-11 | Apple Inc. | User interface for correcting recognition errors |
US10755703B2 (en) | 2017-05-11 | 2020-08-25 | Apple Inc. | Offline personal assistant |
US10395654B2 (en) | 2017-05-11 | 2019-08-27 | Apple Inc. | Text normalization based on a data-driven learning network |
US10847142B2 (en) | 2017-05-11 | 2020-11-24 | Apple Inc. | Maintaining privacy of personal information |
US11599331B2 (en) | 2017-05-11 | 2023-03-07 | Apple Inc. | Maintaining privacy of personal information |
US10726832B2 (en) | 2017-05-11 | 2020-07-28 | Apple Inc. | Maintaining privacy of personal information |
US10410637B2 (en) | 2017-05-12 | 2019-09-10 | Apple Inc. | User-specific acoustic models |
US10791176B2 (en) | 2017-05-12 | 2020-09-29 | Apple Inc. | Synchronization and task delegation of a digital assistant |
US11380310B2 (en) | 2017-05-12 | 2022-07-05 | Apple Inc. | Low-latency intelligent automated assistant |
US11405466B2 (en) | 2017-05-12 | 2022-08-02 | Apple Inc. | Synchronization and task delegation of a digital assistant |
US10789945B2 (en) | 2017-05-12 | 2020-09-29 | Apple Inc. | Low-latency intelligent automated assistant |
US11301477B2 (en) | 2017-05-12 | 2022-04-12 | Apple Inc. | Feedback analysis of a digital assistant |
US11580990B2 (en) | 2017-05-12 | 2023-02-14 | Apple Inc. | User-specific acoustic models |
US10810274B2 (en) | 2017-05-15 | 2020-10-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
US10482874B2 (en) | 2017-05-15 | 2019-11-19 | Apple Inc. | Hierarchical belief states for digital assistants |
US10303715B2 (en) | 2017-05-16 | 2019-05-28 | Apple Inc. | Intelligent automated assistant for media exploration |
US10403278B2 (en) | 2017-05-16 | 2019-09-03 | Apple Inc. | Methods and systems for phonetic matching in digital assistant services |
US11532306B2 (en) | 2017-05-16 | 2022-12-20 | Apple Inc. | Detecting a trigger of a digital assistant |
US12254887B2 (en) | 2017-05-16 | 2025-03-18 | Apple Inc. | Far-field extension of digital assistant services for providing a notification of an event to a user |
US10909171B2 (en) | 2017-05-16 | 2021-02-02 | Apple Inc. | Intelligent automated assistant for media exploration |
US10311144B2 (en) | 2017-05-16 | 2019-06-04 | Apple Inc. | Emoji word sense disambiguation |
US10748546B2 (en) | 2017-05-16 | 2020-08-18 | Apple Inc. | Digital assistant services based on device capabilities |
US11675829B2 (en) | 2017-05-16 | 2023-06-13 | Apple Inc. | Intelligent automated assistant for media exploration |
US11217255B2 (en) | 2017-05-16 | 2022-01-04 | Apple Inc. | Far-field extension for digital assistant services |
US10657328B2 (en) | 2017-06-02 | 2020-05-19 | Apple Inc. | Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling |
US10445429B2 (en) | 2017-09-21 | 2019-10-15 | Apple Inc. | Natural language understanding using vocabularies with compressed serialized tries |
US10755051B2 (en) | 2017-09-29 | 2020-08-25 | Apple Inc. | Rule-based natural language processing |
US10636424B2 (en) | 2017-11-30 | 2020-04-28 | Apple Inc. | Multi-turn canned dialog |
US10733982B2 (en) | 2018-01-08 | 2020-08-04 | Apple Inc. | Multi-directional dialog |
US10733375B2 (en) | 2018-01-31 | 2020-08-04 | Apple Inc. | Knowledge-based framework for improving natural language understanding |
US10789959B2 (en) | 2018-03-02 | 2020-09-29 | Apple Inc. | Training speaker recognition models for digital assistants |
US10592604B2 (en) | 2018-03-12 | 2020-03-17 | Apple Inc. | Inverse text normalization for automatic speech recognition |
US11710482B2 (en) | 2018-03-26 | 2023-07-25 | Apple Inc. | Natural assistant interaction |
US10818288B2 (en) | 2018-03-26 | 2020-10-27 | Apple Inc. | Natural assistant interaction |
US10909331B2 (en) | 2018-03-30 | 2021-02-02 | Apple Inc. | Implicit identification of translation payload with neural machine translation |
US11854539B2 (en) | 2018-05-07 | 2023-12-26 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US11487364B2 (en) | 2018-05-07 | 2022-11-01 | Apple Inc. | Raise to speak |
US11900923B2 (en) | 2018-05-07 | 2024-02-13 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US10928918B2 (en) | 2018-05-07 | 2021-02-23 | Apple Inc. | Raise to speak |
US11169616B2 (en) | 2018-05-07 | 2021-11-09 | Apple Inc. | Raise to speak |
US11145294B2 (en) | 2018-05-07 | 2021-10-12 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US10984780B2 (en) | 2018-05-21 | 2021-04-20 | Apple Inc. | Global semantic word embeddings using bi-directional recurrent neural networks |
US11495218B2 (en) | 2018-06-01 | 2022-11-08 | Apple Inc. | Virtual assistant operation in multi-device environments |
US11360577B2 (en) | 2018-06-01 | 2022-06-14 | Apple Inc. | Attention aware virtual assistant dismissal |
US10892996B2 (en) | 2018-06-01 | 2021-01-12 | Apple Inc. | Variable latency device coordination |
US12080287B2 (en) | 2018-06-01 | 2024-09-03 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US11431642B2 (en) | 2018-06-01 | 2022-08-30 | Apple Inc. | Variable latency device coordination |
US10984798B2 (en) | 2018-06-01 | 2021-04-20 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US11386266B2 (en) | 2018-06-01 | 2022-07-12 | Apple Inc. | Text correction |
US11009970B2 (en) | 2018-06-01 | 2021-05-18 | Apple Inc. | Attention aware virtual assistant dismissal |
US10720160B2 (en) | 2018-06-01 | 2020-07-21 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US10403283B1 (en) | 2018-06-01 | 2019-09-03 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US10684703B2 (en) | 2018-06-01 | 2020-06-16 | Apple Inc. | Attention aware virtual assistant dismissal |
US10496705B1 (en) | 2018-06-03 | 2019-12-03 | Apple Inc. | Accelerated task performance |
US10504518B1 (en) | 2018-06-03 | 2019-12-10 | Apple Inc. | Accelerated task performance |
US10944859B2 (en) | 2018-06-03 | 2021-03-09 | Apple Inc. | Accelerated task performance |
US11010561B2 (en) | 2018-09-27 | 2021-05-18 | Apple Inc. | Sentiment prediction from textual data |
US10839159B2 (en) | 2018-09-28 | 2020-11-17 | Apple Inc. | Named entity normalization in a spoken dialog system |
US11462215B2 (en) | 2018-09-28 | 2022-10-04 | Apple Inc. | Multi-modal inputs for voice commands |
US11170166B2 (en) | 2018-09-28 | 2021-11-09 | Apple Inc. | Neural typographical error modeling via generative adversarial networks |
US11475898B2 (en) | 2018-10-26 | 2022-10-18 | Apple Inc. | Low-latency multi-speaker speech recognition |
US11638059B2 (en) | 2019-01-04 | 2023-04-25 | Apple Inc. | Content playback on multiple devices |
US11348573B2 (en) | 2019-03-18 | 2022-05-31 | Apple Inc. | Multimodality in digital assistant systems |
US11217251B2 (en) | 2019-05-06 | 2022-01-04 | Apple Inc. | Spoken notifications |
US11307752B2 (en) | 2019-05-06 | 2022-04-19 | Apple Inc. | User configurable task triggers |
US11475884B2 (en) | 2019-05-06 | 2022-10-18 | Apple Inc. | Reducing digital assistant latency when a language is incorrectly determined |
US11705130B2 (en) | 2019-05-06 | 2023-07-18 | Apple Inc. | Spoken notifications |
US11423908B2 (en) | 2019-05-06 | 2022-08-23 | Apple Inc. | Interpreting spoken requests |
US11140099B2 (en) | 2019-05-21 | 2021-10-05 | Apple Inc. | Providing message response suggestions |
US11888791B2 (en) | 2019-05-21 | 2024-01-30 | Apple Inc. | Providing message response suggestions |
US11360739B2 (en) | 2019-05-31 | 2022-06-14 | Apple Inc. | User activity shortcut suggestions |
US11237797B2 (en) | 2019-05-31 | 2022-02-01 | Apple Inc. | User activity shortcut suggestions |
US11496600B2 (en) | 2019-05-31 | 2022-11-08 | Apple Inc. | Remote execution of machine-learned models |
US11657813B2 (en) | 2019-05-31 | 2023-05-23 | Apple Inc. | Voice identification in digital assistant systems |
US11289073B2 (en) | 2019-05-31 | 2022-03-29 | Apple Inc. | Device text to speech |
US11360641B2 (en) | 2019-06-01 | 2022-06-14 | Apple Inc. | Increasing the relevance of new available information |
US11488406B2 (en) | 2019-09-25 | 2022-11-01 | Apple Inc. | Text detection using global geometry estimators |
US11924254B2 (en) | 2020-05-11 | 2024-03-05 | Apple Inc. | Digital assistant hardware abstraction |
US11765209B2 (en) | 2020-05-11 | 2023-09-19 | Apple Inc. | Digital assistant hardware abstraction |
US11755276B2 (en) | 2020-05-12 | 2023-09-12 | Apple Inc. | Reducing description length based on confidence |
US12277954B2 (en) | 2024-04-16 | 2025-04-15 | Apple Inc. | Voice trigger for a digital assistant |
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US20110060589A1 (en) | 2011-03-10 |
CN102024013A (en) | 2011-04-20 |
JP2011059659A (en) | 2011-03-24 |
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