One-Pass Coarse-to-Fine Segmental Speech Decoding Algorithm

In this paper, a novel one-pass coarse-to-fine decoding algorithm is proposed to accelerate the speed of segment model (SM). The algorithm is originated from the segmentation similarity observation described in the paper and is specific for the SM based speech recognition. At each step, a coarse search is first implemented to get coarse segmentations and then a fine search is performed based on the derived segmentation information. This fast algorithm is successfully integrated into an SM based Mandarin LVCSR system and saves more than 50% decoding time without obvious influence on the recognition accuracy