title: Emotion identification using extremely low frequency components of speech feature contours.
abstract: The investigations of emotional speech identification can be divided into two main parts, features and classifiers. In this paper, how to extract an effective speech feature set for the emotional speech identification is addressed. In our speech feature set, we use not only statistical analysis of frame-based acoustical features, but also the approximated speech feature contours, which are obtained by extracting extremely low frequency components to speech feature contours. Furthermore, principal component analysis (PCA) is applied to the approximated speech feature contours so that an efficient representation of approximated contours can be derived. The proposed speech feature set is fed into support vector machines (SVMs) to perform multiclass emotion identification. The experimental results demonstrate the performance of the proposed system with 82.26% identification rate.
authors: Department of Computer Science and Information Engineering, National Central University, Taiwan.
authors: Chang-Hong
authors: CH
authors: Lin
authors: Department of Computer Science and Information Engineering, National Central University, Taiwan.
authors: Wei-Kai
authors: WK
authors: Liao
authors: Department of Computer Science and Information Engineering, National Central University, Taiwan.
authors: Wen-Chi
authors: WC
authors: Hsieh
authors: Department of Computer Science and Information Engineering, National Central University, Taiwan.
authors: Wei-Jiun
authors: WJ
authors: Liao
authors: Department of Computer Science and Information Engineering, National Central University, Taiwan.
authors: Jia-Ching
authors: 0000-0003-0024-6732
authors: JC
authors: Wang
mesh_terms: D000465:Algorithms; D004644:Emotions; D005260:Female; D006801:Humans; D008297:Male; D025341:Principal Component Analysis; D013060:Speech; D060388:Support Vector Machine