A feature parameter modification algorithm for voice activity detection based on support vector machine

A feature parameter modification algorithm is proposed to increase the accuracy of voice activity detection that based on support vector machine and energy acceleration parameters. The three energy acceleration parameters should have the equal importance for voice activity detection, in that all the three parameters can suitably characterize the classification features of speech and non-speech frames. For the three energy acceleration parameters, its minimum values vary a little; but its maximum values are greatly different. When radial basis function is chosen as the kernel function for voice activity detection, the coordinate values in Hilbert space is dominative determined by the energy values over a sub-region spectrum, the other two parameters only have a few contributions to it. The proposed algorithm extends the three energy acceleration parameters into the nearby or same order of magnitude. It make the three energy acceleration parameters at the same importance level in the calculation of coordinate values in Hilbert space, and can finally increase the accuracy of voice activity detection. The experimental results show that this algorithm can increase the accuracy of voice activity detection in the absence of noise and noise conditions.