Integration of the predictedwalk model estimate into the particle filter framework

Distortion robustness is one of the most significant problems in automatic speech recognition. While a lot of research in speech feature enhancement in automatic recognition has focused on stationary distortions, most of the observed distortions are non-stationary. To cope with the non-stationary behavior, just recently, various particle filter approaches have been proposed to track the non-stationary distortions on speech features in logarithmic spectral or cepstral domain. Most of those techniques rely on the prediction of the noise evolution model by a linear prediction matrix. The current estimation of the linear prediction matrix, however, needs noise only observations which have to be either given a priori or to be detected by voice activity detection. This makes it impossible to adapt the linear prediction matrix to the dynamics of the noise on speech regions. In this publication we propose to estimate or update the linear prediction matrix directly on the noisy speech observations. This is possible within the particle filter framework by weighting the different noisy estimates (particles) due to their likelihood in the estimation equation of the linear prediction matrix. Speech recognition experiments on actual recordings with different speaker to microphone distances confirm the soundness of the proposed approach.