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This work presents an automatic speech recognition system which uses a missing data approach to compensate for environmental noise. The missing, noise-corrupted components are identified using binaural features or a support vector machine (SVM) classifier. To perform speech recognition using the partially observed data, the missing components are(More)
The problem of reverberation in speech recognition is addressed in this study by extending a noise-robust feature enhancement method based on non-negative matrix factorization. The signal model of the observation as a linear combination of sample spectrograms is augmented by a melspectral feature domain convolution to account for the effects of room(More)
This paper addresses dereverberation of speech using an unsupervised approach utilizing speech prior and taking only weak assumptions on reverberation. Our approach uses a long time context representation of reverberated speech in spectral-temporal supervectors which are decorrelated by the PCA. In the decorrelated domain supervectors are mapped from(More)
Following earlier work, we modify linear predictive (LP) speech analysis by including temporal weighting of the squared prediction error in the model optimization. In order to focus this so called weighted LP model on the least noisy signal regions in the presence of stationary additive noise, we use shorttime signal energy as the weighting function. We(More)
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