말소리와 음성과학 (Phonetics and Speech Sciences)

  • 제6권2호
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  • Pages.29-34
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  • 2014
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  • 2005-8063(pISSN)
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  • 2586-5854(eISSN)
한국음성학회 (Korean Society of Speech Sciences)
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잡음음성 음향모델 적응에 기반한 잡음에 강인한 음성인식

Noise Robust Speech Recognition Based on Noisy Speech Acoustic Model Adaptation

  • 투고 : 2014.03.18
  • 심사 : 2014.06.16
  • 발행 : 2014.06.30
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초록

In the Vector Taylor Series (VTS)-based noisy speech recognition methods, Hidden Markov Models (HMM) are usually trained with clean speech. However, better performance is expected by training the HMM with noisy speech. In a previous study, we could find that Minimum Mean Square Error (MMSE) estimation of the training noisy speech in the log-spectrum domain produce improved recognition results, but since the proposed algorithm was done in the log-spectrum domain, it could not be used for the HMM adaptation. In this paper, we modify the previous algorithm to derive a novel mathematical relation between 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, the proposed method produced 10.6% of relative improvement in Word Error Rates (WERs) over the MTR method while the previous MMSE estimation of the training noisy speech produced 4.3% of relative improvement, which shows the superiority of the proposed method.

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참고문헌

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