Phonetics and Speech Sciences (말소리와 음성과학)

Korean Society of Speech Sciences (한국음성학회)
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HMM-based missing feature reconstruction for robust speech recognition in additive noise environments

가산잡음환경에서 강인음성인식을 위한 은닉 마르코프 모델 기반 손실 특징 복원

  • Received : 2014.11.04
  • Accepted : 2014.12.13
  • Published : 2014.12.31
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Abstract

This paper describes a robust speech recognition technique by reconstructing spectral components mismatched with a training environment. Although the cluster-based reconstruction method can compensate the unreliable components from reliable components in the same spectral vector by assuming an independent, identically distributed Gaussian-mixture process of training spectral vectors, the presented method exploits the temporal dependency of speech to reconstruct the components by introducing a hidden-Markov-model prior which incorporates an internal state transition plausible for an observed spectral vector sequence. The experimental results indicate that the described method can provide temporally consistent reconstruction and further improve recognition performance on average compared to the conventional method.

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References

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