Model adaptation employing DNN-based estimation of noise corruption function for noise-robust speech recognition

Ki-mu Yoon; Wooil Kim

doi:10.7776/ASK.2019.38.1.047

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2019 Vol.38, Issue 1 Preview Page Next Page

Research Article

Model adaptation employing DNN-based estimation of noise corruption function for noise-robust speech recognition 잡음 환경 음성 인식을 위한 심층 신경망 기반의 잡음 오염 함수 예측을 통한 음향 모델 적응 기법

31 January 2019. pp. 47-50

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Abstract

This paper proposes an acoustic model adaptation method for effective speech recognition in noisy environments. In the proposed algorithm, the noise corruption function is estimated employing DNN (Deep Neural Network), and the function is applied to the model parameter estimation. The experimental results using the Aurora 2.0 framework and database demonstrate that the proposed model adaptation method shows more effective in known and unknown noisy environments compared to the conventional methods. In particular, the experiments of the unknown environments show 15.87 % of relative improvement in the average of WER (Word Error Rate).

Keywords

Noise corruption function

DNN (Deep Neural Network)

Model adaptation

Speech recognition

Noisy environments

본 논문에서는 잡음 환경에서 효과적인 음성 인식을 위하여 DNN(Deep Neural Network) 기반의 잡음 오염 함수 예측을 이용한 음향 모델 적응 기법을 제안한다. 깨끗한 음성과 잡음 정보를 입력으로 하고 오염된 음성에 대한 특징 벡터를 출력으로 하는 DNN을 학습하여 비선형 관계를 갖는 잡음 오염 함수를 예측한다. 예측된 잡음 오염 함수를 음향모델의 평균 벡터에 적용하여 잡음 환경에 적응된 음향 모델을 생성한다. Aurora 2.0 데이터를 이용한 음성 인식 성능 평가에서 본 논문에서 제안한 모델 적응 기법이 기존의 전처리, 모델 적응 기법에 비해 일치, 불일치 잡음 환경에서 모두 평균적으로 우수한 성능을 나타낸다. 특히 불일치 잡음 환경에서 평균 오류율이 15.87 %의 상대 향상률을 나타낸다.

키워드

잡음 오염 함수

심층 신경망

모델 적응

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 : 38
No :1
Pages :47-50
Received Date : 2018-11-09
Accepted Date : 2019-01-23
DOI :https://doi.org/10.7776/ASK.2019.38.1.047

The Journal of the Acoustical Society of Korea ISSN:1225-4428(Print) 2287-3775(Online) 한국음향학회지

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