• Title/Summary/Keyword: Incremental robust adaptation

Search Result 2, Processing Time 0.02 seconds

Performance Enhancement for Speaker Verification Using Incremental Robust Adaptation in GMM (가무시안 혼합모델에서 점진적 강인적응을 통한 화자확인 성능개선)

  • Kim, Eun-Young;Seo, Chang-Woo;Lim, Yong-Hwan;Jeon, Seong-Chae
    • The Journal of the Acoustical Society of Korea
    • /
    • v.28 no.3
    • /
    • pp.268-272
    • /
    • 2009
  • In this paper, we propose a Gaussian Mixture Model (GMM) based incremental robust adaptation with a forgetting factor for the speaker verification. Speaker recognition system uses a speaker model adaptation method with small amounts of data in order to obtain a good performance. However, a conventional adaptation method has vulnerable to the outlier from the irregular utterance variations and the presence noise, which results in inaccurate speaker model. As time goes by, a rate in which new data are adapted to a model is reduced. The proposed algorithm uses an incremental robust adaptation in order to reduce effect of outlier and use forgetting factor in order to maintain adaptive rate of new data on GMM based speaker model. The incremental robust adaptation uses a method which registers small amount of data in a speaker recognition model and adapts a model to new data to be tested. Experimental results from the data set gathered over seven months show that the proposed algorithm is robust against outliers and maintains adaptive rate of new data.

Safety Robust Speaker Recognition Against Utterance Variationsed (발성변화에 강인한 화자 인식에 관한 연구)

  • Lee Ki-Yong
    • Journal of Internet Computing and Services
    • /
    • v.5 no.2
    • /
    • pp.69-73
    • /
    • 2004
  • A speaker model In speaker recognition system is to be trained from a large data set gathered in multiple sessions. Large data set requires large amount of memory and computation, and moreover it's practically hard to make users utter the data inseveral sessions. Recently the incremental adaptation methods are proposed to cover the problems, However, the data set gathered from multiple sessions is vulnerable to the outliers from the irregular utterance variations and the presence of noise, which result in inaccurate speaker model. In this paper, we propose an incremental robust adaptation method to minimize the influence of outliers on Gaussian Mixture Madel based speaker model. The robust adaptation is obtained from an incremental version of M-estimation. Speaker model is initially trained from small amount of data and it is adapted recursively with the data available in each session, Experimental results from the data set gathered over seven months show that the proposed method is robust against outliers.

  • PDF