We predict HR from speech using the SRI BioFrustration Corpus.In contrast to previous studies we use continuous spontaneous speech as input.
This work addresses lexical unit discovery for languages without (usable) written resources.
In the present study, we focus exclusively on progress in developing speech recognition for the language of interest, Yoloxóchitl Mixtec (YM), an Oto-Manguean language spoken by fewer than 5000 speakers on the Pacific coast of Guerrero, Mexico.
We introduce the SRI CLEO (Conversational Language about Everyday Objects) Speaker-State Corpus of speech, video, and biosignals.
Prediction of heart rate changes from speech features during interaction with a misbehaving dialog system
This study examines two questions: how do undesirable system responses affect people physiologically, and to what extent can we predict physiological changes from the speech signal alone?
We describe the SRI BioFrustration Corpus, an inprogress corpus of time-aligned audio, video, and autonomic nervous system signals recorded while users interact with a dialog system to make returns of faulty consumer items.
We explore a diverse set of features based only on spoken audio to understand which features correlate with self-reported depression scores according to the Beck depression rating scale.
In this work, we present robust acoustic features motivated by the knowledge gained from human speech perception and production, and demonstrate that these features provide reasonable robustness to reverberation effects compared to traditional mel-filterbank-based features.
We present design strategies for a keyword spotting (KWS) system that operates in highly degraded channel conditions with very low signal-to-noise ratio levels.