Abstract: An automated interactive voice dialogue system using a human-in-the-loop design may enable human-supported interventions, where the automated system still conducts most of the interaction but enables a human agent to assume control of the dialogue and assist, if deemed necessary, so that the user may continue the interaction with little interruption or frustration. In some examples, the user of the dialogue system of this disclosure may not realize that there was a problem, and that the interaction is being or has switched from an automated dialogue system to a human. In some examples, the automated dialogue system of this disclosure may also automatically switch back to machine interaction when the human agent has resolved the situation.

Abstract: A voice-based digital assistant (VDA) uses a conversation intelligence (CI) manager module having a rule-based engine on conversational intelligence to process information from one or more modules to make determinations on both i) understanding the human conversational cues and ii) generating the human conversational cues, including at least understanding and generating a backchannel utterance, in a flow and exchange of human communication in order to at least one of grab or yield a conversational floor between a user and the VDA. The CI manager module uses the rule-based engine to analyze and make a determination on a conversational cue of, at least, prosody in a user's flow of speech to generate the backchannel utterance to signal any of i) an understanding, ii) a correction, iii) a confirmation, and iv) a questioning of verbal communications conveyed by the user in the flow of speech during a time frame when the user still holds the conversational floor.

Abstract: Systems and methods for speech recognition are provided. In some aspects, the method comprises receiving, using an input, an audio signal. The method further comprises splitting the audio signal into auditory test segments. The method further comprises extracting, from each of the auditory test segments, a set of acoustic features. The method further comprises applying the set of acoustic features to a deep neural network to produce a hypothesis for the corresponding auditory test segment. The method further comprises selectively performing one or more of: indirect adaptation of the deep neural network and direct adaptation of the deep neural network.

Abstract: Provided are systems, computer-implemented methods, and computer-program products for a multi-lingual device, capable of receiving verbal input in multiple languages, and further capable of providing conversational responses in multiple languages. In various implementations, the multi-lingual device includes an automatic speech recognition engine capable of receiving verbal input in a first natural language and providing a textual representation of the input and a confidence value for the recognition. The multi-lingual device can also include a machine translation engine, capable of translating textual input from the first natural language into a second natural language. The machine translation engine can output a confidence value for the translation. The multi-lingual device can further include natural language processing, capable of translating from the second natural language to a computer-based language.

Abstract: In general, the disclosure describes techniques for personalizing a meeting summary according to the relevance of different meeting items within a meeting to different users. In some examples, a computing system for automatically providing personalized summaries of meetings comprises a memory configured to store information describing a meeting; and processing circuitry configured to receive a plurality of meeting item summaries of respective meeting items included in the transcript of the meeting; determine, by applying a model of meeting item relevance to the meeting item summaries, a corresponding relevance to a user of each of the meeting item summaries; and output respective indications of relevance to the user for one or more of the meeting item summaries to provide a personalized summary of the meeting to the user.

Abstract: In general, the disclosure describes techniques for personalizing a meeting summary according to the relevance of different meeting items within a meeting to different users. In some examples, a computing system for automatically providing personalized summaries of meetings comprises a memory configured to store information describing a meeting; and processing circuitry configured to receive a plurality of meeting item summaries of respective meeting items included in the transcript of the meeting; determine, by applying a model of meeting item relevance to the meeting item summaries, a corresponding relevance to a user of each of the meeting item summaries; and output respective indications of relevance to the user for one or more of the meeting item summaries to provide a personalized summary of the meeting to the user.

Abstract: An electronic device for providing health information or assistance includes an input configured to receive at least one type of signal selected from sound signals, verbal signals, non-verbal signals, and combinations thereof, a communication module configured to send information related to the at least one user and his/her environment to a remote device, including the sound signals, non-verbal signals, and verbal signals, the remote device being configured to analyze a condition of the at least one user and communicate condition signals to the electronic device, a processing module configured to receive the condition signals and to cause the electronic device to engage in a passive monitoring mode or an active engagement and monitoring mode, the active engagement and monitoring mode including, but not limited to, verbal communication with the at least one user, and an output configured to engage the at least one user in verbal communication.

Abstract: Systems and methods for speech recognition are provided. In some aspects, the method comprises receiving, using an input, an audio signal. The method further comprises splitting the audio signal into auditory test segments. The method further comprises extracting, from each of the auditory test segments, a set of acoustic features. The method further comprises applying the set of acoustic features to a deep neural network to produce a hypothesis for the corresponding auditory test segment. The method further comprises selectively performing one or more of: indirect adaptation of the deep neural network and direct adaptation of the deep neural network.

Abstract: A computer-implemented method can include a speech collection module collecting a speech pattern from a patient, a speech feature computation module computing at least one speech feature from the collected speech pattern, a mental health determination module determining a state-of-mind of the patient based at least in part on the at least one computed speech feature, and an output module providing an indication of a diagnosis with regard to a possibility that the patient is suffering from a certain condition such as depression or Post-Traumatic Stress Disorder (PTSD).

Abstract: Provided are systems, computer-implemented methods, and computer-program products for a multi-lingual device, capable of receiving verbal input in multiple languages, and further capable of providing conversational responses in multiple languages. In various implementations, the multi-lingual device includes an automatic speech recognition engine capable of receiving verbal input in a first natural language and providing a textual representation of the input and a confidence value for the recognition. The multi-lingual device can also include a machine translation engine, capable of translating textual input from the first natural language into a second natural language. The machine translation engine can output a confidence value for the translation. The multi-lingual device can further include natural language processing, capable of translating from the second natural language to a computer-based language.

Abstract: Provided are systems, computer-implemented methods, and computer-program products for a multi-lingual device, capable of receiving verbal input in multiple languages, and further capable of providing conversational responses in multiple languages. In various implementations, the multi-lingual device includes an automatic speech recognition engine capable of receiving verbal input in a first natural language and providing a textual representation of the input and a confidence value for the recognition. The multi-lingual device can also include a machine translation engine, capable of translating textual input from the first natural language into a second natural language. The machine translation engine can output a confidence value for the translation. The multi-lingual device can further include natural language processing, capable of translating from the second natural language to a computer-based language.

Abstract: An electronic device for providing health information or assistance includes an input configured to receive at least one type of signal selected from sound signals, verbal signals, non-verbal signals, and combinations thereof, a communication module configured to send information related to the at least one user and his/her environment to a remote device, including the sound signals, non-verbal signals, and verbal signals, the remote device being configured to analyze a condition of the at least one user and communicate condition signals to the electronic device, a processing module configured to receive the condition signals and to cause the electronic device to engage in a passive monitoring mode or an active engagement and monitoring mode, the active engagement and monitoring mode including, but not limited to, verbal communication with the at least one user, and an output configured to engage the at least one user in verbal communication.

Abstract: Provided are systems, computer-implemented methods, and computer-program products for a multi-lingual device, capable of receiving verbal input in multiple languages, and further capable of providing conversational responses in multiple languages. In various implementations, the multi-lingual device includes an automatic speech recognition engine capable of receiving verbal input in a first natural language and providing a textual representation of the input and a confidence value for the recognition. The multi-lingual device can also include a machine translation engine, capable of translating textual input from the first natural language into a second natural language. The machine translation engine can output a confidence value for the translation. The multi-lingual device can further include natural language processing, capable of translating from the second natural language to a computer-based language.

Abstract: A computer-implemented method can include a speech collection module collecting a speech pattern from a patient, a speech feature computation module computing at least one speech feature from the collected speech pattern, a mental health determination module determining a state-of-mind of the patient based at least in part on the at least one computed speech feature, and an output module providing an indication of a diagnosis with regard to a possibility that the patient is suffering from a certain condition such as depression or Post-Traumatic Stress Disorder (PTSD).