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KMID : 1022420140060020029
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Phonetics and Speech Sciences
2014 Volume.6 No. 2 p.29 ~ p.34
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Noise Robust Speech Recognition Based on Noisy Speech Acoustic Model Adaptation
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Chung Yong-Joo
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Abstract
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In the Vector Taylor Series (VTS)-based noisy speech recognition methods, Hidden Markov Models (HMM) are usuallytrained with clean speech. However, better performance is expected by training the HMM with noisy speech. In a previousstudy, we could find that Minimum Mean Square Error (MMSE) estimation of the training noisy speech in the log-spectrumdomain produce improved recognition results, but since the proposed algorithm was done in the log-spectrum domain, it couldnot be used for the HMM adaptation. In this paper, we modify the previous algorithm to derive a novel mathematical relationbetween test and training noisy speech in the cepstrum domain and the mean and covariance of the Multi-condition TRaining(MTR) trained noisy speech HMM are adapted. In the noisy speech recognition experiments on the Aurora 2 database, theproposed method produced 10.6% of relative improvement in Word Error Rates (WERs) over the MTR method while theprevious MMSE estimation of the training noisy speech produced 4.3% of relative improvement, which shows the superiorityof the proposed method.
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KEYWORD
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noisy speech recognition, model adaptation, VTS, HMM
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