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Stress Detection Based on Multi-class Probabilistic Support Vector Machines for Accented English Speech

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4 Author(s)
Jhing-Fa Wang ; Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan ; Gung-Ming Chang ; Jia-Ching Wang ; Shun-Chieh Lin

A stress detection based on multi-class probabilistic support vector machines (MCP-SVMs) is proposed for classifying speech into following categories - no stress, primary stress, and secondary stress. The stress classifier is performed with a feature set including perceptual features, MFCC, delta-MFCC and delta-delta-MFCC. To observe that speakers from the same accent regions had similar tendencies in mispronunciations including word stress, this work uses English Across Taiwan (EAT) to represent Taiwanese-accented English speech corpora. The overall performance in the experimental results achieves about 84% classification of accuracy.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:7 )

Date of Conference:

March 31 2009-April 2 2009