An effective feature compensation scheme tightly matched with speech recognizer employing SVM-based GMM generation

This paper proposes an effective feature compensation scheme to address a real-life situation where clean speech database is not available for Gaussian Mixture Model (GMM) training for a model-based feature compensation method. The proposed scheme employs a Support Vector Machine (SVM)based model selection method to effectively generate the GMM for our feature compensation method directly from the Hidden Markov Model (HMM) of the speech recognizer. We also present a strategy to address the case of a combination with Cepstral Mean Normalization (CMN), where the HMM for speech recognizer is obtained using CMN-processed speech database. Experimental results demonstrate that the proposed method is effective at providing a comparable speech recognition performance to the matched data condition where the clean speech database is available for GMM training which is also used for HMM training for speech recognizer. This proves that the SVM-based model selection method is able to effectively generate Gaussian components from the pre-trained HMMmodel parameters to make the GMM for the feature compensation method be tightly matched to the speech recognizer, resulting in robust speech recognition performance with various types of background noise conditions.