University of Michigan

International Business Machines Corporation

Emily Mower Provost

Lazaros Polymenakos

Dimitrios B Dimitriadis

Melvin Mcinnis

Soheil Khorram

Zakaria Aldeneh

John H Gideon

Satinder Singh Baveja

Katherine Anne Matton

Biqiao Zhang

Zahi N Karam

Systems, computer-implemented methods, and computer program products that can facilitate predicting a source of a subsequent spoken dialogue are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a speech receiving component that can receive a spoken dialogue from a first entity. The computer executable components can further comprise a speech processing component that can employ a network that can concurrently process a transition type and a dialogue act of the spoken dialogue to predict a source of a subsequent spoken dialogue.

End-of-turn detection in spoken dialogues

A method of predicting a mood state of a user may include recording an audio sample via a microphone of a mobile computing device of the user based on the occurrence of an event, extracting a set of acoustic features from the audio sample, generating one or more emotion values by analyzing the set of acoustic features using a trained machine learning model, and determining the mood state of the user, based on the one or more emotion values. In some embodiments, the audio sample may be ambient audio recorded periodically, and/or call data of the user recorded during clinical calls or personal calls.

Automatic speech-based longitudinal emotion and mood recognition for mental health treatment

A method includes receiving acoustic features and phonetic features associated with an utterance from a driver in a vehicle, providing the acoustic features and the phonetic features to a feature fusion sub-network, receiving a feature fusion utterance representation from the feature fusion sub-network, providing one of the acoustic features or the phonetic features to a non-fusion sub-network trained using supervised learning, receiving a non-fusion utterance representation from the non-fusion sub-network, generating an intermediate utterance representation based on the feature fusion utterance representation and the non-fusion utterance representation, providing at least a portion of the intermediate utterance representation to a fully-connected sub-network trained using supervised learning, receiving a valence vector from the fully-connected sub-network, and causing a vehicle control system to perform a vehicle maneuver based on the valence vector.

Exploiting acoustic and lexical properties of phonemes to recognize valence from speech

A system that monitors and assesses the moods of subjects with neurological disorders, like bipolar disorder, by analyzing normal conversational speech to identify speech data that is then analyzed through an automated speech data classifier. The classifier may be based on a vector, separator, hyperplane, decision boundary, or other set of rules to classify one or more mood states of a subject. The system classifier is used to assess current mood state, predicted instability, and/or a change in future mood state, in particular for subjects with bipolar disorder.

Mood monitoring of bipolar disorder using speech analysis

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Ann Arbor, MI, United States of America

Emily Mower Provost