Abstract: 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.

Abstract: A system for measuring neuromotor disorders from speech is configured to receive an audio recording that includes spoken speech and compute feature coefficients from at least a portion of the spoken speech in the audio recording. The feature coefficients represent at least one characteristic of the spoken speech in the audio recording. One or more vocal tract variables may be computed from the feature coefficients. The vocal tract variables may represent a physical configuration of a vocal tract associated with at least one of the one or more sounds. The vocal tract variables and/or the feature coefficients are used to determine if a disorder that affects neuromotor speech is present.

Abstract: 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.