Detection of time-pressure induced stress in speech via acoustic indicators

Authors:
Matthew Frampton;Sandeep Sripada;Ricardo Augusto Hoffmann Bion;Stanley Peters
Affiliations:
Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA
Venue:
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Year:
2010

Citing 4
Cited 0

Analysis and compensation of speech under stress and noise for environmental robustness in speech recognition

Speech Communication - Special issue on speech under stress
Nonlinear speech analysis and acoustic model adaptation with applications to stress classification and speech recognition

Nonlinear speech analysis and acoustic model adaptation with applications to stress classification and speech recognition
Multistyle classification of speech under stress using feature subset selection based on genetic algorithms

Speech Communication
Speech Under Stress: Analysis, Modeling and Recognition

Speaker Classification I

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Abstract

We use automatically extracted acoustic features to detect speech which is generated under stress, achieving 76.24% accuracy with a binary logistic regression. Our data are task-oriented human-human dialogues in which a time-limit is unexpectedly introduced partway through. Analysis suggests that we can detect approximately when this event occurs. We also consider the importance of normalizing the acoustic features by speaker, and detecting stress in new speakers.