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 non-adaptive classification system and the V/UV detectors adopted by three important speech coding standards: LPC10, ITU-T G.723.1 and ETSI AMR. In all cases the adaptive V/UV classifier outperformed the traditional solutions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
