Sparse coding over redundant dictionaries for fast adaptation of speech recognition system

This work presents a novel use of the sparse coding over redundant dictionary for fast adaptation of the acoustic models in the hidden Markov model-based automatic speech recognition (ASR) systems. The presented work is an extension of the existing acoustic model-interpolation-based fast adaptation approaches. In these methods, the basis (model) weights are estimated using an iterative procedure employing the maximum-likelihood (ML) criterion. For effective adaptation, typically a number of bases are selected and as a result of that the latency of the iterative weight estimation process becomes high for those ASR tasks that involve human-machine interactions. To address this issue, we propose the use of sparse coding of the target mean supervector over a speaker-specific (exemplar) redundant dictionary. In this approach, the employed greedy sparse coding not only selects the desired bases but also compresses them into a single supervector, which is then ML scaled to yield the adapted mean parameters. Thus reducing the latency in the basis weight estimation in comparison to the existing fast adaptation techniques. Further, to address the loss in information due to reduced degrees of freedom, we have also extended the proposed approach using separate sparse codings over multiple (exemplar and learned) redundant dictionaries. In adapting an ASR task involving human-computer interactions, the proposed approach is found to be as effective as the existing techniques but with a substantial reduction in the computational cost.