Improved voice activity detection algorithm using wavelet and support vector machine

Authors:
Shi-Huang Chen;Rodrigo Capobianco Guido;Trieu-Kien Truong;Yaotsu Chang
Affiliations:
Department of Computer Science and Information Engineering, Shu-Te University, Kaohsiung County, 824, Taiwan, ROC;University of São Paulo (USP), Institute of Physics at São Carlos (IFSC), Department of Physics and Informatics (FFI), Avenida Trabalhador SãoCarlense 400, 13566-590 São Carlos ...;Department of Information Engineering, I-Shou University, Kaohsiung County, 840, Taiwan, ROC;Department of Applied Mathematics, I-Shou University, Kaohsiung County, 840, Taiwan, ROC
Venue:
Computer Speech and Language
Year:
2010

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Abstract

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETSI) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD.