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2009 WRI World Congress on Computer Science and Information Engineering
Stress Detection Based on Multi-class Probabilistic Support Vector Machines for Accented English Speech
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
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.
Index Terms:
stress detection, English Across Taiwan, multi-class probabilistic support vector machines
Citation:
Jhing-Fa Wang, Gung-Ming Chang, Jia-Ching Wang, Shun-Chieh Lin, "Stress Detection Based on Multi-class Probabilistic Support Vector Machines for Accented English Speech," csie, vol. 7, pp.346-350, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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