Model-based feature enhancement for noisy speech recognition

In this paper, a new feature enhancement algorithm called model-based feature enhancement (MBFE) is introduced for noise robust speech recognition. In MBFE, statistical models (i.e., Gaussian HMM´s) of the clean speech feature vectors and of the perturbing noise feature vectors are used to construct the optimal MMSE estimator of the clean speech feature vectors. The estimated clean speech features are then fed to a recognizer. The performance of MBFE is studied experimentally on a connected-digits recognition task in several additive noise conditions (synthetic white and impulsive noise, car noise, and machine tool noise are considered). The performance of MBFE is also compared to that of a state-of-the-art implementation of nonlinear spectral subtraction