Vocal emotion recognition in five native languages of Assam using new wavelet features

Abstract

The present work investigates the following specific research questions concerning voice emotion recognition: whether vocal emotion expressions of discrete emotion (i) can be distinguished from no-emotion (i.e. neutral), (ii) can be distinguished from another, (iii) of surprise, which is actually a cognitive component that could be present with any emotion, can also be recognized as distinct emotion, (iv) can be recognized cross-lingually. This study will enable us to get more information regarding nature and function of emotion. Furthermore, this work will help in developing a generalized voice emotion recognition system, which will increase the efficiency of human-machine interaction systems. In this work an emotional utterance database is created with 140 acted utterances per speaker consisting of short sentences of six full-blown basic emotions and neutral of five native languages of Assam. This database is validated by a Listening Test. Four feature sets are extracted based on WPCC2 (Wavelet-Packet-Cepstral-Coefficients computed by method 2), MFCC (Mel-Frequency-Cepstral-Coefficients), tfWPCC2 (Teager-energy-operated-in-Transform-domain WPCC2) and tfMFCC. The Gaussian Mixture Model (GMM) is used as classifier. The performances of all these feature sets are compared in respect of accuracy of classification in two experiments: (i) text-and-speaker independent vocal emotion recognition in individual languages, and (ii) cross-lingual vocal emotion recognition. tfWPCC2 is a new wavelet feature set proposed by the same authors in one of their recent papers in a National Seminar in India as cited in References.