NDLI: Improvement of Acoustic Model in Text-independent Pronunciation Quality Assessment

Content Provider	IEEE Xplore Digital Library
Author	Yaohui Qi Changhai Shi Fengpei Ge Yonghong Yan
Copyright Year	2012
Abstract	In order to give an accurate assessment, the test speech should be recognized firstly in the text-independent pronunciation quality assessment system. Field test data has some flaws which degrade the recognition performance, such as noise, accent and spontaneous speaking style. In this paper, we investigate these factors by improving the acoustic model (AM) for the speech recognition system. Background noise is added to the training data to enhance the ability of anti-noise. Speaker-based Cepstral Mean and Variance Normalization (SCMVN) is adopted to alleviate the distortion of channel and the impact of inter-speaker pronunciation variability. Maximum a Posteriori (MAP) adaptation is applied twice, in order to tune acoustic model to match the pronunciation characteristic of the accent and the spontaneous style in spoken language. According to the experimental results, above measures increase the word correct rate relatively by 44.1% and the correlation coefficient between machine score and expert score relatively by 6.3%.
Starting Page	193
Ending Page	196
File Size	261514
Page Count	4
File Format	PDF
ISBN	9781467304702
DOI	10.1109/ICICTA.2012.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Noise Hidden Markov models Training data Speech recognition acoustic model Speech Speaker-based Cepstral Mean and Variance Normalization Acoustics MAP text-independent pronunciation quality assessment
Content Type	Text
Resource Type	Article

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