Vocal emotion recognition in five native languages of Assam using prosodic features in presence of white noise

Summary form only given. This work investigates whether vocal emotion expressions of (i) discrete emotion be distinguished from `no-emotion´ (i.e. neutral), (ii) one discrete emotion be distinguished from another, (iii) surprise, which is actually a cognitive component that could be present with any emotion, be also recognized as distinct emotion, (iv) discrete emotion 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 vocal emotion recognition system, which will increase the efficiency of human-machine interaction systems. In this work, an emotional speech database consisting of short sentences of six full-blown basic emotions and neutral is created with 140 simulated utterances per speaker of five native languages of Assam. This database is validated by a Listening Test. The Gaussian Mixture Model (GMM) is used as classifier. The performance of the prosodic feature set is computed at sampling frequency of 8.1 kHz from the utterances with and without additive white noise of 5 db and 0 db Signal-to-Noise Ratios (SNRs) under matched noise training and testing condition.