This paper proposes support vector machine (SVM) based voice activity detection using FuzzyEn to improve detection performance under noisy conditions. The proposed voice activity detection (VAD) uses fuzzy entropy (FuzzyEn) as a feature extracted from noise-reduced speech signals to train an SVM model for speech/non-speech classification. The proposed VAD method was tested by conducting various experiments by adding real background noises of different signal-to-noise ratios (SNR) ranging from −10 dB to 10 dB to actual speech signals collected from the TIMIT database. The analysis proves that FuzzyEn feature shows better results in discriminating noise and corrupted noisy speech. The efficacy of the SVM classifier was validated using 10-fold cross validation. Furthermore, the results obtained by the proposed method was compared with those of previous standardized VAD algorithms as well as recently developed methods. Performance comparison suggests that the proposed method is proven to be more efficient in detecting speech under various noisy environments with an accuracy of 93.29%, and the FuzzyEn feature detects speech efficiently even at low SNR levels.
In this paper a novel voice activity detection approach using smoothed fuzzy entropy (smFuzzyEn) feature using support vector machine is proposed. The proposed approach (smFESVM) uses total variation filter and Savitzky-Golay filter to smooth the FuzzyEn feature extracted from the noisy speech signals. Also, convolution of the first order difference of TV filter and noisy fuzzy entropy feature (conFETV') is also proposed. The obtained smoothed feature vectors are further normalized using min-max normalization and the normalized feature vectors train SVM model for speech/non-speech classification. The proposed smFESVM method shows better discrimination of noise and noisy speech when tested under various nonstationary background noises of different signal-to-noise ratio levels. 10 – fold cross validation was used to validate the efficacy of the SVM classifier. The performance of the smFESVM is compared against various algorithms and comparison suggests that the results obtained by the smFESVM is efficient in detecting speech under low SNR conditions with an accuracy of 93.88%.
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