Senior Research Linguist, Speech Technology and Research Laboratory
Andreas Kathol, Ph.D., is a research linguist in SRI International’s Speech Technology and Research (STAR) Laboratory. His research interests include natural language syntax and semantics, grammar modeling for speech applications, machine translation (rule-based and statistical), dialog systems, crowdsourcing for data collection and other tasks, usability of speech-enabled systems, and Arabic linguistics.
At SRI, Kathol has most recently been the lead developer of the grammar component in a number of projects based on SRI’s virtual personal assistant (VPA) technology for banking, retail, and other domains. He has also led a number of data collection efforts, using both crowdsourcing and traditional laboratory-based methods.
Prior to joining SRI, Kathol was on the faculty of the University of California, Berkeley’s Linguistics Department. He holds a Ph.D. in linguistics from the Ohio State University.
Recent publications
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Toward human-assisted lexical unit discovery without text resources
This work addresses lexical unit discovery for languages without (usable) written resources.
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Analysis and prediction of heart rate using speech features from natural speech
We predict HR from speech using the SRI BioFrustration Corpus.In contrast to previous studies we use continuous spontaneous speech as input.
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The SRI CLEO Speaker-State Corpus
We introduce the SRI CLEO (Conversational Language about Everyday Objects) Speaker-State Corpus of speech, video, and biosignals.
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Automatic Speech Transcription for Low-Resource Languages — The Case of Yoloxóchitl Mixtec (Mexico)
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.
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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?
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The SRI biofrustration corpus: Audio, video and physiological signals for continuous user modeling
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.