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Cepstral mean and variance normalization (CMVN) is an efficient noise compensation technique popularly used in many speech applications. CMVN eliminates the mismatch between training and test utterances by transforming them to zero mean and unit variance. In this work, we argue that some amount of useful information is lost during normalization as every utterance is forced to have the same first-...
In this paper, a modification to the training process of the popular SPLICE algorithm has been proposed for noise robust speech recognition. The modification is based on feature correlations, and enables this stereo-based algorithm to improve the performance in all noise conditions, especially in unseen cases. Further, the modified framework is extended to work for non-stereo datasets where clean...
Cepstral Mean and Variance Normalization (CMVN) is a computationally efficient normalization technique for noise robust speech recognition. The performance of CMVN is known to degrade for short utterances, due to insufficient data for parameter estimation and loss of discriminable information as all utterances are forced to have zero mean and unit variance. In this work, we propose to use posterior...
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