DECODER TECHNOLOGY FOR CONNECTIONIST LARGE VOCABULARY SPEECH RECOGNITION
The search problem in large vocabulary continuous speech recognition (LVCSR) is to locate the most probable string of words for a spoken utterance given the acoustic signal and a set of sentence models. Searching the space of possible utterances is difficult because of the large vocabulary size and the complexity imposed when long-span language models are used. This report describes an efficient search procedure and its software embodiment in a decoder, NOWAY, which has been incorporated in ABBOT, a hybrid connectionist/ hidden Markov model (HMM) LVCSR system [15]. The search algorithm is based on stack decoding and uses both likelihoodand posterior-based pruning. The use of the posterior-based phone deactivation pruning techniques is well-suited to hybrid connectionist/HMM systems because posterior phone probabilities are directly computed by the connectionist acoustic model. The single-pass decoder has been evaluate on the large vocabulary North American Business News task using a 20,000 word vocabulary and a trigram language model. These results indicate that phone deactivation pruning increased the search speed by an order of magnitude while incurring 2% or less relative search error. Using a pentium-based PC system, evaluation quality decoding (less than 3% relative search error) was available with execution speeds 2–5 times slower than realtime, and realtime decoding was available at the cost of 4–12% relative search error.