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  • Research Article
  • Open Access

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

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

  • Received: 9 October 2008
  • Accepted: 24 June 2009
  • Published:


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.


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

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Authors’ Affiliations

Dipartimento di Ingegneria Informatica e delle Telecomunicazioni, Universita' degli Studi di Catania, Viale Andrea Doria, 6, 95125 Catania, Italy
Dipartimento di Fisica della Materia e Ingegneria Elettronica, Universita' di Messina, Salita Sperone, 31, 98166 Messina, Italy