Emotion Classification Using Segmentation of Vowel-Like and Non-Vowel-Like Regions

Abstract

In this work, a novel region switching based classification method is proposed for speech emotion classification using vowel-like regions (VLRs) and non-vowel-like regions (non-VLRs). In literature, normally the entire active speech region is processed for emotion classification. A few studies have been performed on segmented sound units, such as, syllables, phones, vowel, consonant and voiced, for speech emotion classification. This work presents a detailed analysis of emotion information contained independently in segmented VLRs and non-VLRs. The proposed region switching based method is implemented by choosing the features of either VLRs or non-VLRs for each emotion. The VLRs are detected by identifying hypothesized VLR onset and end points. Segmentation of non-VLRs is done by using the knowledge of VLRs and active speech regions. The performance is evaluated using EMODB, IEMOCAP and FAU AIBO databases. Experimental results show that both the VLRs and non-VLRs contain emotion-specific information. In terms of emotion classification rate, the proposed region switching based classification approach shows significant improvement in comparison to the classification approach by processing entire active speech region, and it outperforms other state-of-the-art approaches for all the three databases.