Speech recognition in a home environment using parallel decoding with GMM-based noise modeling

In this paper, we propose a method for noise-robust speech recognition in a home environment based on noise modeling and parallel decoding. There are three basic ideas of the proposed method. First, we model the noise signals observed in the environment using a GMM. Second, we generate multiple noise-reduced signals using the mean vectors of the GMM and decode the signals in parallel. Third, we choose the best recognition result from the multiple recognition results based on the confidence score. The proposed method is very simple and straightforward, yet effective compared with simple noise reduction. The experiments proved that the proposed method is effective for not only noise signals in the database but also for those in the real home environment.