PCA-based Variational Model Composition Method for Roust Speech Recognition with Time-Varying Background Noise

시변 잡음에 강인한 음성 인식을 위한 PCA 기반의 Variational 모델 생성 기법

  • Kim, Wooil (School of Computer Science and Engineering, Incheon National University)
  • Received : 2013.10.21
  • Accepted : 2013.11.20
  • Published : 2013.12.31


This paper proposes an effective feature compensation method to improve speech recognition performance in time-varying background noise condition. The proposed method employs principal component analysis to improve the variational model composition method. The proposed method is employed to generate multiple environmental models for the PCGMM-based feature compensation scheme. Experimental results prove that the proposed scheme is more effective at improving speech recognition accuracy in various SNR conditions of background music, compared to the conventional front-end methods. It shows 12.14% of average relative improvement in WER compared to the previous variational model composition method.


Supported by : 인천대학교


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