Statistical estimation of unreliable features for robust speech recognition

This paper addresses the problem of robust speech recognition in noisy conditions in the framework of hidden Markov models (HMMs) and missing feature techniques. It presents a new statistical approach to detection and estimation of unreliable features based on a probabilistic measure and Gaussian mixture model (GMM). In the estimation process, the GMM is compensated using parameters of the statistical model of additive background noise. The GMM means are used to replace the unreliable features. The GMM based technique is less complex than the corresponding HMM based estimation and gives similar improvement in the recognition performance. Once unreliable features are replaced by the estimated clean speech features, the entire set of spectral features can be transformed to the other feature domain characterized by higher baseline recognition rate (e.g. MFCCs) for final recognition using continuous density hidden Markov models (CDHMMs) with diagonal covariance matrices.