Noise Robust Exemplar Matching for Speech Enhancement: Applications to Automatic Speech Recognition
Abstract
We present a novel automatic speech recognition (ASR) scheme which uses the recently proposed noise robust exemplar matching framework for speech enhancement in the front-end. The proposed system employs a GMM-HMM back-end to recognize the enhanced speech signals unlike the prior work focusing on template matching only. Speech enhancement is achieved using multiple dictionaries containing speech exemplars representing a single speech unit and several noise exemplars of the same length. These combined dictionaries are used to approximate the noisy segments and the speech component is obtained as a linear combination of the speech exemplars in the combined dictionaries yielding the minimum total reconstruction error. The performance of the proposed system is evaluated on the small vocabulary track of the 2nd CHiME Challenge and the AURORA-2 database and the results have shown the effectiveness of the proposed approach in improving the noise robustness of a conventional ASR system.
Deepak Baby
Applied Scientist
My research interests include speech recognition, enhancement and deep learning.
