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Bios September 8, 2021

Andreas Tsiartas

Research Engineer, Speech Technology and Research Laboratory

Andreas Tsiartas, Ph.D., has research interests that include speech cues analysis, cognitive state representation, speech-to-speech translation, acoustic and language modeling, and speech signals representation.

He holds a B.Sc. degree in electronics and computer engineering from the Technical University of Crete. He also holds an MSc. and a Ph.D. in signal processing from the Department of Electrical Engineering (EE), University of Southern California (USC). He has published more than 20 papers in the field of speech processing.

Tsiartas’ honors and awards include best teaching assistant awards in 2009 and 2010 from EE, USC. In 2006, he was awarded the Viterbi School Dean’s Doctoral Fellowship from USC.

Recent publications

more +
  • Speech‐based markers for post traumatic stress disorder in US veterans

    This study demonstrates that a speech-based algorithm can objectively differentiate PTSD cases from controls.

  • Mapping Individual to Group Level Collaboration Indicators Using Speech Data

    To address the challenge of mapping characteristics of individuals’ speech to information about the group, we coded behavioral and learning-related indicators of collaboration at the individual level.

  • Crowdsourcing Emotional Speech

    We describe the methodology for the collection and annotation of a large corpus of emotional speech data through crowdsourcing.

  • Inferring Stance from Prosody

    Speech conveys many things beyond content, including aspects of stance and attitude that have not been much studied.

  • 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.

  • Privacy- preserving speech analytics for automatic assessment of student collaboration

    This work investigates whether nonlexical information from speech can automatically predict the quality of small-group collaborations. Audio was collected from students as they collaborated in groups of three to solve math problems.

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