Cluster-based dynamic variance adaptation for interconnecting speech enhancement pre-processor and speech recognizer

A conventional approach to noise robust speech recognition consists of employing a speech enhancement pre-processor prior to recognition. However, such a pre-processor usually introduces artifacts that limit recognition performance improvement. In this paper we discuss a framework for improving the interconnection between speech enhancement pre-processors and a recognizer. The framework relies on recent proposals for increasing robustness by replacing the point estimate of the enhanced features with a distribution with a dynamic (i.e. time varying) feature variance. We have recently proposed a model for the dynamic feature variance consisting of a dynamic feature variance root obtained from the pre-processor, which is multiplied by a weight representing the pre-processor uncertainty, and that uses adaptation data to optimize the pre-processor uncertainty weight. The formulation of the method is general and could be used with any speech enhancement pre-processor. However, we observed that in case of noise reduction based on spectral subtraction or related approaches, adaptation could fail because the proposed model is weak at representing well the actual dynamic feature variance. The dynamic feature variance changes according to the level of speech sound, which varies with the HMM states. Therefore, we propose improving the model by introducing HMM state dependency. We achieve this by using a cluster-based representation, i.e. the Gaussians of the acoustic model are grouped into clusters and a different pre-processor uncertainty weight is associated with each cluster. Experiments with various pre-processors and recognition tasks prove the generality of the proposed integration scheme and show that the proposed extension improves the performance with various speech enhancement pre-processors.

► We investigate an interconnection scheme for speech enhancement and recognizer.
► The method uses adaptation data to optimize a dynamic model of the feature variance.
► We extend the dynamic feature variance model to a cluster-based representation.
► Significant performance improvement is demonstrated for various recognition tasks.