Robust speech recognition using noise-cluster HMM interpolation

This paper proposes a novel approach called noise-cluster HMM interpolation for robust speech recognition. The approach helps alleviating the problem of speech recognition under noisy environments not trained in the system. In this method, a new HMM is interpolated from existing noisy-speech HMMs that are best matched to the input speech. This process is performed on-the-fly with an acceptable delay time and, hence, no need to prepare and store the final model in advance. Interpolation weights among HMMs can be determined by either a direct or a tree-structured search. Evaluated focusing on speech in unseen noisy-environments, the proposed method obviously outperforms a baseline system whose acoustic model for such unseen environment is selected from a tree structure.