Markov model based noise modeling and its application to noisy speech recognition using dynamical features of speech

Tetsunori Kobayashi, Ryuji Mine, Katsuhiko Shirai

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

In this paper, some algorithms to recognize speech in time varying noise are proposed. In the proposed methods, Spectral subtraction and Markov model based noise models are successfully utilized in the framework of spectral decomposition of noisy speech. Firstly, we considered the problem of the mis-subtraction noise which is caused in the subtraction based decomposition procedure. Then, the precise use of dynamical feature of speech such as delta cepstrum is discussed. Using the methods proposed here, recognition performance are improved more than 60 % compared to no compensation method.

Original languageEnglish
Article number389719
Pages (from-to)II57-II60
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
DOIs
Publication statusPublished - 1994
EventProceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6) - Adelaide, Aust
Duration: 1994 Apr 191994 Apr 22

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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