Stress Detection Based on Multi-class Probabilistic Support Vector Machines for Accented English Speech
Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.739
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.
stress detection, English Across Taiwan, multi-class probabilistic support vector machines
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, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 346-350, doi:10.1109/CSIE.2009.739