Open Access

Adaptive V/UV Speech Detection Based on Characterization of Background Noise

EURASIP Journal on Audio, Speech, and Music Processing20092009:965436

DOI: 10.1155/2009/965436

Received: 9 October 2008

Accepted: 24 June 2009

Published: 9 September 2009

Abstract

The paper presents an adaptive system for Voiced/Unvoiced (V/UV) speech detection in the presence of background noise. Genetic algorithms were used to select the features that offer the best V/UV detection according to the output of a background Noise Classifier (NC) and a Signal-to-Noise Ratio Estimation (SNRE) system. The system was implemented, and the tests performed using the TIMIT speech corpus and its phonetic classification. The results were compared with a nonadaptive classification system and the V/UV detectors adopted by two important speech coding standards: the V/UV detection system in the ETSI ES 202 212 v1.1.2 and the speech classification in the Selectable Mode Vocoder (SMV) algorithm. In all cases the proposed adaptive V/UV classifier outperforms the traditional solutions giving an improvement of 25% in very noisy environments.

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Authors’ Affiliations

(1)
Dipartimento di Ingegneria Informatica e delle Telecomunicazioni, Universita' degli Studi di Catania
(2)
Dipartimento di Fisica della Materia e Ingegneria Elettronica, Universita' di Messina

Copyright

© F. Beritelli et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.