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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/17173

Title: Coping Imbalanced Prosodic Unit Boundary Detection with Linguistically Motivated Prosodic Features
Authors: Liu, Yi-Fen;Tseng, Shu-Chuan;Jang, Roger J. S.;Chen, Alvin Cheng-Hsien
Contributors: 英語學系
Keywords: Prosodic unit;Machine learning;Biased minimax probability machine
Date: 2010-09
Issue Date: 2013-08-28T05:01:18Z
Publisher: ISCA
Abstract: Continuous speech input for ASR processing is usually presegmented into speech stretches by pauses. In this paper, we propose that smaller, prosodically defined units can be identified by tackling the problem on imbalanced prosodic unit boundary detection using five machine learning techniques. A parsimonious set of linguistically motivated prosodic features has been proven to be useful to characterize prosodic boundary information. Furthermore, BMPM is prone to have true positive rate on the minority class, i.e. the defined prosodic units. As a whole, the decision tree classifier, C4.5, reaches a more stable performance than the other algorithms.
Relation: INTERSPEECH 2010, : 1417-1420
Appears in Collections:[英語學系] 會議論文

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