Optimizing speech/non-speech classifier design using AdaBoost

We propose a new method to design speech/non-speech classifiers for voice activity detection and robust endpoint detection using the adaptive boosting (AdaBoost) algorithm. The method uses a combination of simple base classifiers through the AdaBoost algorithm and a set of optimized speech features combined with spectral subtraction. The key benefits of this method are the simple implementation and low computational complexity. The AdaBoost classifier combined with spectral subtraction significantly improved the receiver operating characteristic curves of the G.729 voice activity detector. For speech recognition purpose, the method reduced 20-50% of miss errors for the same false alarm rate by using additional band pass energy and spectral distortion based on mel frequency cepstral coefficients.