Progress in dynamic network decoding

We show how we boosted the efficiency of the dynamic network decoder in IBM's Attila speech recognition framework, by transforming the underlying concept from token-passing to word-conditioned, and adding speedup methods like sparse LM look-ahead. On several different tasks, we achieve improvements of 30 to 50% in efficiency at equal precision. We compare the efficiency to a state-of-the-art WFST based static decoder, and note that the added methods improve the dynamic decoder under conditions where it was lacking before in comparison, specifically when using a relatively small LM. Overall, the new dynamic decoder performs similarly to the static decoder, with a lead for the dynamic decoder on tasks with a larger LM, and a lead for the static decoder on tasks with a smaller LM.