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

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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Correspondence to S Serrano.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Beritelli, F., Casale, S., Russo, A. et al. Adaptive V/UV Speech Detection Based on Characterization of Background Noise. J AUDIO SPEECH MUSIC PROC. 2009, 965436 (2009). https://doi.org/10.1155/2009/965436

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Keywords

  • Genetic Algorithm
  • Background Noise
  • Acoustics
  • Ratio Estimation
  • Adaptive System