Enhancement of Rejection Performance using the PSO-NCM in Noisy Environment

잡음 환경하에서의 PSO-NCM을 이용한 거절기능 성능 향상

  • 김병돈 (전남대학교 전자정보통신공학과) ;
  • 송민규 (전남대학교 전자정보통신공학과) ;
  • 최승호 (동신대학교 컴퓨터학과) ;
  • 김진영 (전남대학교 전자정보통신공학과)
  • Published : 2008.12.30


Automatic speech recognition has severe performance degradation under noisy environments. To cope with the noise problem, many methods have been proposed. Most of them focused on noise-robust features or model adaptation. However, researchers have overlooked utterance verification (UV) under noisy environments. In this paper we discuss UV problems based on the normalized confidence measure. First, we show that UV performance is also degraded in noisy environments with the experiments of an isolated word recognition. Then we observe how the degradation of UV performances is suffered. Based on the UV experiments we propose a modeling method of the statistics of phone confidences using sigmoid functions. For obtaining the parameters of the sigmoidal models, the particle swarm optimization (PSO) is adopted. The proposed method improves 20% rejection performance. Our experimental results show that the PSO-NCM can apply noise speech recognition successfully.