NDLI: Some acoustic improvements for pronunciation quality assessment for strongly accented mandarin speech

Content Provider	IEEE Xplore Digital Library
Author	Fengpei Ge Fuping Pan Changliang Liu Bin Dong Qingwei Zhao Yonghong Yan
Copyright Year	2008
Description	Author affiliation: ThinkIT Lab., Chinese Acad. of Sci., Beijing (Fengpei Ge; Fuping Pan; Changliang Liu; Bin Dong; Qingwei Zhao; Yonghong Yan)
Abstract	This paper presents our recent study in resolving some specific acoustic problems of the computer assisted language learning (CALL) system by modifying the acoustic model (AM) and feature under ASR framework. Firstly, speaker dependent cepstrum mean normalization (Speaker CMN) is adopted to alleviate the distortion of channel, with which the average human-machine scoring correlation coefficient (ACC) is improved from 78.00% to 84.14%. Heteroscedastic linear discriminate analysis (HLDA) is then applied to enhance the discrimination ability of AM, which successfully increases ACC from 84.14% to 84.62%. Additionally, HLDA can lessen the great human-machine scoring difference of those speeches that have very good or too bad pronunciation quality, and so lead to an increase of the correctly-rank rate (CRR) from 85.59% to 90.99%. Finally, we use maximum a posteriori (MAP) to tune AM to match the strong accented test speech. As the result, ACC is improved from 84.62% to 86.57%.
Starting Page	691
Ending Page	696
File Size	245230
Page Count	6
File Format	PDF
ISBN	9781424417230
DOI	10.1109/ICALIP.2008.4590175
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	Training Accuracy Computational modeling Hidden Markov models Speech Acoustics Decoding
Content Type	Text
Resource Type	Article

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