NDLI: State-based bilingual model modification for nonnative speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Qingqing Zhang Ta Li Jielin Pan Yonghong Yan
Copyright Year	2008
Description	Author affiliation: ThinkIT Speech Lab., Inst. of Acoust. Chinese Acad. of Sci., Beijing (Qingqing Zhang; Ta Li; Jielin Pan; Yonghong Yan)
Abstract	The speech recognition accuracy has been observed to decrease for nonnative speakers, especially those who are just beginning to learn foreign language or who have heavy accents. This paper presents a novel bilingual model modification approach to improve nonnative speech recognition via considering these great variations of accented pronunciations. Each state of the baseline nonnative acoustic models is modified with several candidate states from the auxiliary acoustic models, which are trained by speakerspsila mother language. State mapping criterion and n-best candidates are investigated based on a grammar-constrained speech recognition system. Using the state-based bilingual model modification approach, compared to the nonnative acoustic models which have already been well trained by adaptation technique MAP, a relative reduction of 11.7% in phrase error rate (RPhrER) was further achieved.
Starting Page	1300
Ending Page	1305
File Size	180046
Page Count	6
File Format	PDF
ISBN	9781424417230
DOI	10.1109/ICALIP.2008.4589996
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Adaptation model Hidden Markov models Speech recognition Speech Acoustics Data models
Content Type	Text
Resource Type	Article

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