Title :
Support Vector Machine based Voice Activity Detection
Author :
Baig, M. ; Masud, S. ; Awais, M.
Author_Institution :
Dept. of Comput. Sci., Lahore Univ. of Manage. Sci.
Abstract :
Voice activity detection (VAD) is important for efficient speech coding and accurate automatic speech recognition (ASR). Most of the algorithms proposed in the past, for solving the VAD problem, have been based on some deterministic feature of the speech signal such as zero crossing rate. The speech/non-speech decisions are then taken using suitably chosen thresholds. This paper presents the application of support vector machines (SVM) for classifying the voice activity. The speech signal has been divided into labeled overlapping frames and pattern classification has subsequently been performed by using a supervised learning algorithm. It has been observed that the SVM based solution is computationally efficient and provides around 90% accuracy for speech signals directly recorded using a microphone and an accuracy of over 85% for noisy speech
Keywords :
speech coding; speech recognition; support vector machines; SVM; automatic speech recognition; labeled overlapping frames; pattern classification; speech coding; supervised learning algorithm; support vector machine; voice activity classification; voice activity detection; zero crossing rate; Automatic speech recognition; Background noise; Machine learning; Machine learning algorithms; Signal processing algorithms; Speech coding; Speech enhancement; Support vector machine classification; Support vector machines; Working environment noise; Machine Learning; Speech Coding; Support Vector Machine; Voice Activity Detection;
Conference_Titel :
Intelligent Signal Processing and Communications, 2006. ISPACS '06. International Symposium on
Conference_Location :
Yonago
Print_ISBN :
0-7803-9732-0
Electronic_ISBN :
0-7803-9733-9
DOI :
10.1109/ISPACS.2006.364896