@inproceedings{99767da2841f43d09e54a74700f9c737,
title = "Stress detection based on multi-class probabilistic support vector machines for accented English speech",
abstract = "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.",
author = "Wang, {Jhing Fa} and Chang, {Gung Ming} and Wang, {Jia Ching} and Lin, {Shun Chieh}",
year = "2009",
doi = "10.1109/CSIE.2009.739",
language = "???core.languages.en_GB???",
isbn = "9780769535074",
series = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009",
pages = "346--350",
booktitle = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009",
note = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 ; Conference date: 31-03-2009 Through 02-04-2009",
}