An Improved Mel-Wiener Filter for Mel-LPC based Speech Recognition

We previously proposed a Mel-Wiener filter to enhance Mel-LPC spectra in presence of additive noise. The proposed filter was estimated based on minimization of sum of square error on the linear frequency scale and efficiently implemented in the autocorrelation domain without denoising input speech. In the previously proposed system we segregated speech and noise using an energy based VAD and a very simple flooring technique were used for noise segment. In this present work, we improve the VAD using autoregressive (AR) model of noise and flooring technique as well. In addition, a lag window is applied to the estimated noise autocorrelation function to smooth the fine spectra of high order autocorrelation coefficients. As a result, substantial improvement is obtained over previous result.