Open Access Editorial

Emotion and mental state recognition from speech

Julien Epps12*, Roddy Cowie3, Shrikanth Narayanan4, Björn Schuller5 and Jianhua Tao6

Author Affiliations

1 School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia

2 ATP Research Laboratory, National ICT Australia (NICTA), Eveleigh, NSW 2015, Australia

3 Queen's University, Belfast BT7 1NN, Northern Ireland

4 Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA

5 Institute for Human-Machine Communication, Technische Universität München, 80290 München, Germany

6 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China

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EURASIP Journal on Advances in Signal Processing 2012, 2012:15  doi:10.1186/1687-6180-2012-15

Published: 19 January 2012

First paragraph (this article has no abstract)

As research in speech processing has matured, attention has gradually shifted from linguistic-related applications such as speech recognition towards paralinguistic speech processing problems, in particular the recognition of speaker identity, language, emotion, gender, and age. Determination of a speaker's emotion or mental state is a particularly challenging problem, in view of the significant variability in its expression posed by linguistic, contextual, and speaker-specific characteristics within speech. In response, a range of signal processing and pattern recognition methods have been developed in recent years.