Emotional Feature Extraction Method Based on the Concentration of Phoneme Influence for Human-Robot Interaction

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Depending on the emotion of speech, the meaning of the speech or the intention of the speaker differs. Therefore, speech emotion recognition, as well as automatic speech recognition is necessary to communicate precisely between humans and robots for human-robot interaction. In this paper, a novel feature extraction method is proposed for speech emotion recognition using separation of phoneme class. In feature extraction, the signal variation caused by different sentences usually overrides the emotion variation and it lowers the performance of emotion recognition. However, as the proposed method extracts features from speech in parts that correspond to limited ranges of the center of gravity of the spectrum (CoG) and formant frequencies, the effects of phoneme variation on features are reduced. Corresponding to the range of CoG, the obstruent sounds are discriminated from sonorant sounds. Moreover, the sonorant sounds are categorized into four classes by the resonance characteristics revealed by formant frequency. The result shows that the proposed method using 30 different speakers' corpora improves emotion recognition accuracy compared with other methods by 99% significance level. Furthermore, the proposed method was applied to extract several features including prosodic and phonetic features, and was implemented on '`Mung' robots as an emotion recognizer of users. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2010
Publisher
Vsp Bv
Issue Date
2010
Language
English
Article Type
Article
Keywords

RECOGNITION; EXPRESSION; SPEECH

Citation

ADVANCED ROBOTICS, v.24, no.1-2, pp.47 - 67

ISSN
0169-1864
DOI
10.1163/016918609X12585530487822
URI
http://hdl.handle.net/10203/94322
Appears in Collection
ME-Journal Papers(저널논문)
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