Massachusetts Institute of Technology

Gregory Alan Ciccarelli

Thomas Francis Quatieri, Jr

Christopher J Smalt

Nima Mesgarani

James O'Sullivan

Paul T Calamia

James R Williamson

Jeffrey Shane Palmer

Michael S Brandstein

Satrajit S Ghosh

Joseph Perricone

Michael Nolan

Stephanie Haro

In one aspect of the present disclosure, method includes: receiving neural data responsive to a listener's auditory attention; receiving an acoustic signal responsive to a plurality of acoustic sources; for each of the plurality of acoustic sources: generating, from the received acoustic signal, audio data comprising one or more features of the acoustic source, forming combined data representative of the neural data and the audio data, and providing the combined data to a classification network configured to calculate a similarity score between the neural data and the acoustic source using one or more similarity metrics; and using the similarity scores calculated for each of the acoustic sources to identify, from the plurality of acoustic sources, an acoustic source associated with the listener's auditory attention.

End-to-end deep neural network for auditory attention decoding

In a system and method for assessing the condition of a subject, control parameters are derived from a neurophysiological computational model that operates on features extracted from a speech signal. The control parameters are used as biomarkers (indicators) of the subject's condition. Speech related features are compared with model predicted speech features, and the error signal is used to update control parameters within the neurophysiological computational model. The updated control parameters are processed in a comparison with parameters associated with the disorder in a library.

Assessing disorders through speech and a computational model

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Woburn, MA, United States of America

Gregory Alan Ciccarelli