Noise robust recognition using feature selective modeling

In automatic speech recognition (ASR) systems immunity to additive noise may either be applied at the preprocessing stage or at the pattern matching stage. The Feature Selective Modeling (FSM) approach suggested in this paper is applied in the pattern matching stage, but in contrast to most existing methods, it is optimized on a model basis such that noise robust and phonetically descriptive parameters of a particular model can be set in focus. For sonorant sounds this is done by marking the lowest n mean values of each HMM density function as being sensitive to noise in a log filterbank representation. The noise robustness is obtained by de-emphasizing the marked feature dimensions. Two different methods for de-emphasizing mean value masking and dimensional reduction are presented and experimentally compared to the PMC-algorithm [2].