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Research Article

A study on Gaussian mixture model deep neural network hybrid-based feature compensation for robust speech recognition in noisy environments

잡음 환경에 효과적인 음성 인식을 위한 Gaussian mixture model deep neural network 하이브리드 기반의 특징 보상

Ki-mu Yoon1
,
Wooil Kim1∗
윤 기무1
,
김 우일1∗
1인천대학교 컴퓨터공학부
∗Corresponding Author
30 November 2018. pp. 506-511
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Abstract

This paper proposes an GMM(Gaussian Mixture Model)-DNN(Deep Neural Network) hybrid-based feature compensation method for effective speech recognition in noisy environments. In the proposed algorithm, the posterior probability for the conventional GMM-based feature compensation method is calculated using DNN. The experimental results using the Aurora 2.0 framework and database demonstrate that the proposed GMM-DNN hybrid-based feature compensation method shows more effective in Known and Unknown noisy environments compared to the GMM-based method. In particular, the experiments of the Unknown environments show 9.13 % of relative improvement in the average of WER (Word Error Rate) and considerable improvements in lower SNR (Signal to Noise Ratio) conditions such as 0 and 5 dB SNR.

Keywords
Feature compensation
Deep neural network
Gaussian mixture model
Speech recognition
Noisy environments

본 논문에서는 잡음 환경에서 효과적인 음성인식을 위하여 GMM(Gaussian Mixture Model)-DNN(Deep Neural Network) 하이브리드 기반의 특징 보상 기법을 제안한다. 기존의 GMM 기반의 특징 보상에서 필요로 하는 사후 확률을 DNN을 통해 계산한다. Aurora 2.0 데이터를 이용한 음성 인식 성능 평가에서 본 논문에서 제안한 GMM-DNN 하이브리드 기법이 기존의 GMM 기반 기법에 비해 Known, Unknown 잡음 환경에서 모두 평균적으로 우수한 성능을 나타낸다. 특히 Unknown 잡음 환경에서 평균 오류율이 9.13 %의 상대 향상률을 나타내고, 낮은 SNR(Signal to Noise Ratio) 잡음 환경에서 상당히 우수한 성능을 보인다.

키워드
특징 보상
심층 신경망
가우시안 혼합 모델
음성 인식
잡음 환경
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Information
  • Publisher :The Acoustical Society of Korea
  • Publisher(Ko) :한국음향학회
  • Journal Title :The Journal of the Acoustical Society of Korea
  • Journal Title(Ko) :한국음향학회지
  • Volume : 37
  • No :6
  • Pages :506-511
  • Received Date : 2018-09-27
  • Accepted Date : 2018-11-22
  • DOI :https://doi.org/10.7776/ASK.2018.37.6.506
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