Stereo-based Feature Enhancement Using Dictionary Learning

This paper proposes stereo-based speech feature enhancement using dictionary learning. Instead of posterior values obtained by a Gaussian mixture as in other methods, we use sparse weight vectors and their variants as an alternative noisy speech feature representation. This paper also provides an efficient algorithm that can be applied to large-scale speech processing. We show the effectiveness of the proposed approach by using a middle vocabulary noisy speech recognition task based on WSJ, which was provided by the 2nd CHiME Speech Separation and Recognition Challenge.