Hidden Markov model-based speech emotion recognition

Björn Wolfgang Schuller · Gerhard Rigoll · M. Lang

278 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

We introduce speech emotion recognition by use of continuous hidden Markov models. Two methods are propagated and compared. In the first method, a global statistics framework of an utterance is classified by Gaussian mixture models using derived features of the raw pitch and energy contour of the speech signal. A second method introduces increased temporal complexity, applying continuous hidden Markov models considering several states using low-level instantaneous features instead of global statistics. The paper addresses the design of working recognition engines, and results are achieved with respect to the alluded alternatives. A speech corpus consisting of acted and spontaneous emotion samples in German and English is described in detail. Both engines have been tested and trained using this equivalent speech corpus. Results in recognition of seven discrete emotions exceeded 86% recognition rate. In comparison, the judgment of human deciders classifying the same corpus at 79.8% recognition rate was analyzed.

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2001Emotion recognition in human-computer interaction

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2005Speech emotion recognition based on HMM and SVM 2007Affective State Estimation for Human–Robot Interaction 2008Acoustic feature selection for automatic emotion recognition from speech 2009Being bored? Recognising natural interest by extensive audiovisual integration for real-life application 2011Automatic Speech Emotion Recognition using Support Vector Machine 2010Combining Long Short-Term Memory and Dynamic Bayesian Networks for Incremental Emotion-Sensitive Artificial Listening 2005Speaker Independent Speech Emotion Recognition by Ensemble Classification 2005Speaker independent emotion recognition by early fusion of acoustic and linguistic features within ensembles

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