Abstract: An example process includes: receiving a speech input representing a user utterance; determining, based on a textual representation of the speech input, a first score corresponding to a type of the user utterance; determining, based on the textual representation of the speech input, a second score representing a correspondence between the user utterance and a domain recognized by a digital assistant; determining, based on the first score and the second score, whether the speech input is intended for the digital assistant; in accordance with a determination that the speech input is intended for the digital assistant: initiating, by the digital assistant, a task based on the speech input; and providing an output indicative of the initiated task.

Abstract: An example process includes: receiving a speech input representing a user utterance; determining, based on a textual representation of the speech input, a first score corresponding to a type of the user utterance; determining, based on the textual representation of the speech input, a second score representing a correspondence between the user utterance and a domain recognized by a digital assistant; determining, based on the first score and the second score, whether the speech input is intended for the digital assistant; in accordance with a determination that the speech input is intended for the digital assistant: initiating, by the digital assistant, a task based on the speech input; and providing an output indicative of the initiated task.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. An example method includes receiving, from one or more external electronic devices, a plurality of speaker profiles for a plurality of users; receiving a natural language speech input; determining, based on comparing the natural language speech input to the plurality of speaker profiles: a first likelihood that the natural language speech input corresponds to a first user of the plurality of users; and a second likelihood that the natural language speech input corresponds to a second user of the plurality of users; determining whether the first likelihood and the second likelihood are within a first threshold; and in accordance with determining that the first likelihood and the second likelihood are not within the first threshold: providing a response to the natural language speech input, the response being personalized for the first user.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. An example method includes receiving, from one or more external electronic devices, a plurality of speaker profiles for a plurality of users; receiving a natural language speech input; determining, based on comparing the natural language speech input to the plurality of speaker profiles: a first likelihood that the natural language speech input corresponds to a first user of the plurality of users; and a second likelihood that the natural language speech input corresponds to a second user of the plurality of users; determining whether the first likelihood and the second likelihood are within a first threshold; and in accordance with determining that the first likelihood and the second likelihood are not within the first threshold: providing a response to the natural language speech input, the response being personalized for the first user.

Abstract: An acoustic environment aware method for selecting a high quality audio stream during multi-stream speech recognition. A number of input audio streams are processed to determine if a voice trigger is detected, and if so a voice trigger score is calculated for each stream. An acoustic environment measurement is also calculated for each audio stream. The trigger score and acoustic environment measurement are combined for each audio stream, to select as a preferred audio stream the audio stream with the highest combined score. The preferred audio stream is output to an automatic speech recognizer. Other aspects are also described and claimed.

Abstract: Techniques for training a speaker recognition model used for interacting with a digital assistant are provided. In some examples, user authentication information is obtained at a first time. At a second time, a user utterance representing a user request is received. A voice print is generated from the user utterance. A determination is made as to whether a plurality of conditions are satisfied. The plurality of conditions includes a first condition that the user authentication information corresponds to one or more authentication credentials assigned to a registered user of an electronic device. The plurality of conditions further includes a second condition that the first time and the second time are not separated by more than a predefined time period. In accordance with a determination that the plurality of conditions are satisfied, a speaker profile assigned to the registered user is updated based on the voice print.

Abstract: Systems and processes for generating a speaker profile for use in performing speaker identification for a virtual assistant are provided. One example process can include receiving an audio input including user speech and determining whether a speaker of the user speech is a predetermined user based on a speaker profile for the predetermined user. In response to determining that the speaker of the user speech is the predetermined user, the user speech can be added to the speaker profile and operation of the virtual assistant can be triggered. In response to determining that the speaker of the user speech is not the predetermined user, the user speech can be added to an alternate speaker profile and operation of the virtual assistant may not be triggered. In some examples, contextual information can be used to verify results produced by the speaker identification process.

Abstract: Techniques for training a speaker recognition model used for interacting with a digital assistant are provided. In some examples, user authentication information is obtained at a first time. At a second time, a user utterance representing a user request is received. A voice print is generated from the user utterance. A determination is made as to whether a plurality of conditions are satisfied. The plurality of conditions includes a first condition that the user authentication information corresponds to one or more authentication credentials assigned to a registered user of an electronic device. The plurality of conditions further includes a second condition that the first time and the second time are not separated by more than a predefined time period. In accordance with a determination that the plurality of conditions are satisfied, a speaker profile assigned to the registered user is updated based on the voice print.

Abstract: Systems and processes for automatic accent detection are provided. In accordance with one example, a method includes, at an electronic device with one or more processors and memory, receiving a user input, determining a first similarity between a representation of the user input and a first acoustic model of a plurality of acoustic models, and determining a second similarity between the representation of the user input and a second acoustic model of the plurality of acoustic models. The method further includes determining whether the first similarity is greater than the second similarity. In accordance with a determination that the first similarity is greater than the second similarity, the first acoustic model may be selected; and in accordance with a determination that the first similarity is not greater than the second similarity, the second acoustic model may be selected.

Abstract: Systems and processes for generating a speaker profile for use in performing speaker identification for a virtual assistant are provided. One example process can include receiving an audio input including user speech and determining whether a speaker of the user speech is a predetermined user based on a speaker profile for the predetermined user. In response to determining that the speaker of the user speech is the predetermined user, the user speech can be added to the speaker profile and operation of the virtual assistant can be triggered. In response to determining that the speaker of the user speech is not the predetermined user, the user speech can be added to an alternate speaker profile and operation of the virtual assistant may not be triggered. In some examples, contextual information can be used to verify results produced by the speaker identification process.

Abstract: The present disclosure generally relates to context-based endpoint detection in user speech input. A method for identifying an endpoint of a spoken request by a user may include receiving user input of natural language speech including one or more words; identifying at least one context associated with the user input; generating a probability, based on the at least one context associated with the user input, that a location in the user input is an endpoint; determining whether the probability is greater than a threshold; and in accordance with a determination that the probability is greater than the threshold, identifying the location in the user input as the endpoint.

Abstract: Systems and processes for generating a speaker profile for use in performing speaker identification for a virtual assistant are provided. One example process can include receiving an audio input including user speech and determining whether a speaker of the user speech is a predetermined user based on a speaker profile for the predetermined user. In response to determining that the speaker of the user speech is the predetermined user, the user speech can be added to the speaker profile and operation of the virtual assistant can be triggered. In response to determining that the speaker of the user speech is not the predetermined user, the user speech can be added to an alternate speaker profile and operation of the virtual assistant may not be triggered. In some examples, contextual information can be used to verify results produced by the speaker identification process.

Abstract: A method of performing automatic speech recognition (ASR) using end-pointing markers generated using accelerometer-based voice activity detector starts with a voice activity detector (VAD) generating an accelerometer VAD output (VADa) based on data output by at least one accelerometer that is included in at least one earbud. The at least one accelerometer to detect vibration of the user's vocal chords. A voice processor detects a speech signal based on acoustic signals from at least one microphone. An end-pointer generates the end-pointing markers based on the VADa output and an ASR engine performs ASR on the speech signal based on the end-pointing markers. Other embodiments are also described.

Abstract: The present disclosure generally relates to context-based endpoint detection in user speech input. A method for identifying an endpoint of a spoken request by a user may include receiving user input of natural language speech including one or more words; identifying at least one context associated with the user input; generating a probability, based on the at least one context associated with the user input, that a location in the user input is an endpoint; determining whether the probability is greater than a threshold; and in accordance with a determination that the probability is greater than the threshold, identifying the location in the user input as the endpoint.

Abstract: Systems and processes for automatic accent detection are provided. In accordance with one example, a method includes, at an electronic device with one or more processors and memory, receiving a user input, determining a first similarity between a representation of the user input and a first acoustic model of a plurality of acoustic models, and determining a second similarity between the representation of the user input and a second acoustic model of the plurality of acoustic models. The method further includes determining whether the first similarity is greater than the second similarity. In accordance with a determination that the first similarity is greater than the second similarity, the first acoustic model may be selected; and in accordance with a determination that the first similarity is not greater than the second similarity, the second acoustic model may be selected.

Abstract: Systems and processes for generating a speaker profile for use in performing speaker identification for a virtual assistant are provided. One example process can include receiving an audio input including user speech and determining whether a speaker of the user speech is a predetermined user based on a speaker profile for the predetermined user. In response to determining that the speaker of the user speech is the predetermined user, the user speech can be added to the speaker profile and operation of the virtual assistant can be triggered. In response to determining that the speaker of the user speech is not the predetermined user, the user speech can be added to an alternate speaker profile and operation of the virtual assistant may not be triggered. In some examples, contextual information can be used to verify results produced by the speaker identification process.

Abstract: Videoconferencing may be provided. A participant may be identified from audio information and in video information. From the video information, a plurality of images may be captured of the participant identified in the video information. A unique identifier may be associated with the captured plurality of images. The unique identifier may correspond to the participant identified from the audio information. The captured plurality of images and the associated unique identifier may be saved in a database.

Abstract: An audio stream is segmented into a plurality of time segments using speaker segmentation and recognition (SSR), with each time segment corresponding to the speaker's name, producing an SSR transcript. The audio stream is transcribed into a plurality of word regions using automatic speech recognition (ASR), with each of the word regions having a measure of the confidence in the accuracy of the translation, producing an ASR transcript. Word regions with a relatively low confidence in the accuracy of the translation are identified. The low confidence regions are filtered using named entity recognition (NER) rules to identify low confidence regions that a likely names. The NER rules associate a region that is identified as a likely name with the name of the speaker corresponding to the current, the previous, or the next time segment. All of the likely name regions associated with that speaker's name are selected.

Abstract: In one embodiment, a method includes obtaining media that includes a video stream and an audio stream. The method also includes detecting a number of faces visible in the video stream, and performing a speaker segmentation on the media. Performing the speaker segmentation on the media includes utilizing the number of faces visible in the video stream to augment the speaker segmentation.

Abstract: An audio stream is segmented into a plurality of time segments using speaker segmentation and recognition (SSR), with each time segment corresponding to the speaker's name, producing an SSR transcript. The audio stream is transcribed into a plurality of word regions using automatic speech recognition (ASR), with each of the word regions having a measure of the confidence in the accuracy of the translation, producing an ASR transcript. Word regions with a relatively low confidence in the accuracy of the translation are identified. The low confidence regions are filtered using named entity recognition (NER) rules to identify low confidence regions that a likely names. The NER rules associate a region that is identified as a likely name with the name of the speaker corresponding to the current, the previous, or the next time segment. All of the likely name regions associated with that speaker's name are selected.