State based imputation of missing data for robust speech recognition and speech enhancement

Within the context of continuous-density HMM speech recognition in noise, we report on imputation of missing time-frequency regions using emission state probability distributions. Spectral subtraction and local signal–to– noise estimation based criteria are used to separate the present from the missing components. We consider two approaches to the problem of classification with missing data: marginalization and data imputation. A formalism for data imputation based on the probability distributions of individual Hidden Markov model states is presented. We report on recognition experiments comparing state based data imputation to marginalization in the context of connected digit recognition of speech mixed with factory noise at various global signal-to-noise ratios, and wideband restoration of speech. Potential advantages of the approach are that it can be followed by conventional techniques like cepstral features or artificial neural networks for speech recognition.