NDLI: Emotion recognition using acoustic features and textual content

Content Provider	IEEE Xplore Digital Library
Author	Ze-Jing Chuang Chung-Hsien Wu
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan (Ze-Jing Chuang; Chung-Hsien Wu)
Abstract	The paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-three acoustic features are extracted from the speech input. After principle component analysis (PCA), 14 principle components are selected for discriminative representation. In this representation, each principle component is the combination of the 33 original acoustic features and forms a feature subspace. Support vector machines (SVMs) are adopted to classify the emotional states. In text analysis, all emotional keywords and emotion modification words are manually defined. The emotion intensity levels of emotional keywords and emotion modification words are estimated from a collected emotion corpus. The final emotional state is determined based on the emotion outputs from the acoustic and textual approaches. The experimental result shows that the emotion recognition accuracy of the integrated system is better than each of the two individual approaches.
Starting Page	53
Ending Page	56
File Size	340053
Page Count	4
File Format	PDF
ISBN	0780386035
DOI	10.1109/ICME.2004.1394123
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Emotion recognition Feature extraction Speech analysis Principal component analysis Support vector machines Data mining Computer science Acoustical engineering Signal analysis Support vector machine classification
Content Type	Text
Resource Type	Article

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