Stochastic features for noise robust speech recognition

This paper describes a novel technique for noise robust speech recognition, which can incorporate the characteristics of noise distribution directly in features. The feature itself of each analysis frame has a stochastic form, which can represent the probability density function of the estimated speech component in the noisy speech. Using the sequence of the probability density functions of the estimated speech components and hidden Markov modelling of clean speech, the observation probability of the noisy speech is calculated. In the whole process of the technique, the explicit information on the SNR is not used. The technique is evaluated by large vocabulary isolated word recognition under car noise environment, and is found to have clearly outperformed nonlinear spectral subtraction (with between 13% and 44% reduction in recognition errors)