Robust voiced/unvoiced/mixed/silence classifier with maximum a Posteriori channel/background adaptation

Yongxin Zhang, Michael S. Scordilis

Research output: Contribution to journalConference article

Abstract

A new statistical Voiced/Unvoiced/Mixed/Silence classifier based on a Maximum a Posteriori (MAP) adaptation algorithm is presented. The speech signal distributions are modeled with Gaussian Mixture Models (GMM). The MAP re-estimation of model parameters is based on the sufficient statistics within a form of Bayesian adaptation. The robustness of the proposed technique and model adaptation to different background/channel conditions were evaluated. Experimental results show that the proposed method can adapt and is robust to adverse signal conditions, such as SNR as low as 3dB, noise in a moving vehicle, and band-limited channel conditions.

Original languageEnglish (US)
Pages (from-to)229-232
Number of pages4
JournalConference Proceedings - IEEE SOUTHEASTCON
StatePublished - Nov 9 2005
EventIEEE Southeastcon 2005: Excellence in Engineering, Science and Technology - Ft. Lauderdale, United Kingdom
Duration: Apr 8 2005Apr 10 2005

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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