Sri International

Ellipsis Health, Inc.

Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Elizabeth E Shriberg

Andreas Stolcke

Martin Graciarena

Tomasz Rutowski

Michael Aratow

Amir Hossein Harati Nejad Torbati

Mainul Islam

David Lin

Robert Derward Rogers

Ajay Divakaran

William Scott Mark

Luciana Ferrer

Yang Lu

Farshid Haque

Girish Acharya

Harry Bratt

Michael Frandsen

Cory Albright

Andreas Tsiartas

Dilek Zeynep Hakkani-Tur

Gokhan Tur

Larry Paul Heck

Horacio E Franco

Kenneth C Nitz

Gregory Kent Myers

Michael John Wolverton

Amir Tamrakar

Nicolas Scheffer

Bart Peintner

Neil Yorke-Smith

Dimitra Vergyri

Edgar Kalns

Norman D Winarsky

Min Yin

Behjat Siddiquie

Jihua Huang

Michael Wessel

Hyong-Gyun Kim

Bruce H Knoth

Brent Mombourquette

Eric Law

Heeyoung Lee

Greg Ho

Harpreet Singh Sawhney

Hui Ting Cheng

Chao Wang

Omar Javed

James R Carpenter

Viktor Rozgic

Siddhartha Reddy Jonnalagadda

Steven S Weiner

Andreas Kathol

Jingen Liu

Ishani Chakraborty

Colleen Richey

Katherine M Ryan

Alexandros Potamianos

David A Salter

Vikramjit Mitra

Diego Castan Lavilla

Mitchell McLaren

Avani Parakh

James F Carpenter

Piotr Chlebek

Melissa McCool

David Salter

David Sahner

Joseph B Rogers

David Chisolm

David Chisholm

Mustafa K Sonmez

Bruce Knoth

Mitchell Mclaren

The present disclosure provides acoustic and natural language processing (NLP) models for predicting whether a subject has a behavioral or mental health state of interest based at least in part on input speech from said subject.

Acoustic and natural language processing models for speech-based screening and monitoring of behavioral health conditions

The present application provides techniques for implementing a skill component, configured to perform an assessment of a user, as part of a speech processing system. The system may receive a natural language user input requesting assistance. The skill component may, using one or more machine learning models, determine at least one characteristic of the natural language input (e.g., lexical embedding, acoustic embedding, topic, tone, etc.). The skill component may determine state data for a present session, where the state data indicates a topic of the natural language user input and/or a user state associated with the natural language user input. The skill component may determine past state data of one or more past sessions, and generate a question to the user based on the state data for the natural language user input and the past state data.

Conversation-based skill component for assessing a user's state

Methods, computing devices, and computer-program products are provided for implementing a virtual personal assistant. In various implementations, a virtual personal assistant can be configured to receive sensory input, including at least two different types of information. The virtual personal assistant can further be configured to determine semantic information from the sensory input, and to identify a context-specific framework. The virtual personal assistant can further be configured to determine a current intent. Determining the current intent can include using the semantic information and the context-specific framework. The virtual personal assistant can further be configured to determine a current input state. Determining the current input state can include using the semantic information and one or more behavioral models. The behavioral models can include one or more interpretations of previously-provided semantic information. The virtual personal assistant can further be configured to determine an action using the current intent and the current input state.

VPA with integrated object recognition and facial expression recognition

The present disclosure provides systems and methods for assessing a mental state of a subject in a single session or over multiple different sessions, using for example an automated module to present and/or formulate at least one query based in part on one or more target mental states to be assessed. The query may be configured to elicit at least one response from the subject. The query may be transmitted in an audio, visual, and/or textual format to the subject to elicit the response. Data comprising the response from the subject can be received. The data can be processed using one or more individual, joint, or fused models. One or more assessments of the mental state associated with the subject can be generated for the single session, for each of the multiple different sessions, or upon completion of one or more sessions of the multiple different sessions.

Systems and methods for mental health assessment

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.

Virtual health assistant for promotion of well-being and independent living

Disclosed are machine learning-based technologies that analyze an audio input and provide speaker state predictions in response to the audio input. The speaker state predictions can be selected and customized for each of a variety of different applications.

Real-time speaker state analytics platform

A computing system for recognizing salient events depicted in a video utilizes learning algorithms to detect audio and visual features of the video. The computing system identifies one or more salient events depicted in the video based on the audio and visual features.

Recognizing salient video events through learning-based multimodal analysis of visual features and audio-based analytics

Prosodic features are used for discriminating computer-directed speech from human-directed speech. Statistics and models describing energy/intensity patterns over time, speech/pause distributions, pitch patterns, vocal effort features, and speech segment duration patterns may be used for prosodic modeling. The prosodic features for at least a portion of an utterance are monitored over a period of time to determine a shape associated with the utterance. A score may be determined to assist in classifying the current utterance as human directed or computer directed without relying on knowledge of preceding utterances or utterances following the current utterance. Outside data may be used for training lexical addressee detection systems for the H-H-C scenario. H-C training data can be obtained from a single-user H-C collection and that H-H speech can be modeled using general conversational speech. H-C and H-H language models may also be adapted using interpolation with small amounts of matched H-H-C data.

Prosodic and lexical addressee detection

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).

Systems for speech-based assessment of a patient's state-of-mind

Technologies to detect persuasive multimedia content by using affective and semantic concepts extracted from the audio-visual content as well as the sentiment of associated comments are disclosed. The multimedia content is analyzed and compared with a persuasiveness model.

Exploiting multi-modal affect and semantics to assess the persuasiveness of a video

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Berkeley, CA, United States of America

Elizabeth E Shriberg