Speech emotion recognition using hidden Markov models

Abstract

This paper introduces a first approach to emotion recognition using RAMSES, the UPC’s speech recognition system. The approach is based on standard speech recognition technology using hidden semi-continuous Markov models. Both the selection of low level features and the design of the recognition system are addressed. Results are given on speaker dependent emotion recognition using the Spanish corpus of INTERFACE Emotional Speech Synthesis Database. The accuracy recognising seven different emotions—the six ones defined in MPEG-4 plus neutral style—exceeds 80% using the best combination of low level features and HMM structure. This result is very similar to that obtained with the same database in subjective evaluation by human judges. Dealing with the speaker’s emotion is one of the latest challenges in speech technologies. Three different aspects can be easily identified: speech recognition in the presence of emotional speech, synthesis of emotional speech, and emotion recognition. In this last case, the objective is to determine the emotional state of the speaker out of the speech samples. Possible applications include from help to psychiatric diagnosis to intelligent toys, and is a subject of recent but rapidly growing interest [1]. This paper describes the TALP researchers first approach to emotion recognition. The work is inserted in the scope of the INTERFACE project [2]. The objective of this European Commission sponsored project is “to define new models and implement advanced tools for audio-video analysis, synthesis and representation in order to provide essential technologies for the implementation of large-scale virtual and augmented environments. The work is oriented to make man-machine interaction as natural as possible, based on everyday human communication by speech, facial expressions and body gestures.” In the field of emotion recognition out of speech, the main goal of the INTERFACE project will be the construction of a real-time multi-lingual speaker independent emotion recogniser. For this purpose, large speech databases with recordings from many speakers and languages are needed. As these resources are not available yet, a reduced problem will be addressed first: emotion recognition in multi-speaker language dependent conditions. Namely, this paper deals with the recognition of emotion for two Spanish speakers using standard hidden Markov models technology.

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