Reconocimiento del habla en ambientes ruidosos mediante modelos ocultos de Markov discretos
Tipus de documentText en actes de congrés
Data publicació1992
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Speech recognition in noisy environments remains an unsolved problem, even in the case of isolated word recognition with small vocabularies. Recently, several techniques have been proposed to alleviate this problem. Concretely, the Short-Time Modified Coherence (SMC) parameterization and the Cepstral Projection Distortion (CPD) measure have shown excellent results when tested in a speech recognition system based on Dynamic Time Warping (DTW) and using speech contaminated by additive white noise. In this paper, a new technique based on the AR modeling of the one-sided autocorrelation sequence (OSALPC) is presented and, from a comparative study of these LPC-based techniques in the discrete Hidden Markov Model (DHMM) approach, two main conclusions are attained: 1) the slope cepstral window and a relatively high model order are preferable, and 2) the cepstral representation based on the autocorrelation modeling achieves excellent results.
CitacióHernando, J., Nadeu, C. Reconocimiento del habla en ambientes ruidosos mediante modelos ocultos de Markov discretos. A: Simposium Nacional de Reconocimiento de Formas y Análisis de Imágenes. "SNRFAI 1992: V Simposium nacional de reconocimiento de formas y análisis de imágenes: comunicaciones: Valencia: 21-25 de Septiembre de 1992". Valencia: 1992, p. 212-219.
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