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Echo Noise Robust HMM Learning Model using Average Estimator LMS Algorithm

평균 예측 LMS 알고리즘을 이용한 반향 잡음에 강인한 HMM 학습 모델

  • 안찬식 (광운대학교 컴퓨터공학과) ;
  • 오상엽 (가천대학교 IT대학 인터랙티브미디어학과)
  • Received : 2012.10.10
  • Accepted : 2012.11.10
  • Published : 2012.11.30

Abstract

The speech recognition system can not quickly adapt to varied environmental noise factors that degrade the performance of recognition. In this paper, the echo noise robust HMM learning model using average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise HMM learning model consists of the recognition performance is evaluated. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 3.1dB, recognition rate improved as 3.9%.

Keywords

Echo Noise Cancellation;Average Estimator;LMS(Least Mean Square) filter;adaptive filter;HMM(Hidden Markov Model) Model

Acknowledgement

Supported by : 가천대학교