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 likelihood- and 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...

Acoustic fast match is usually used to accelerate search in large vocabulary continuous speech recognition. This paper discusses a new acoustic fast match algorithm. This proposed fast match is based on incremental evaluation of the score and the use of normalized likelihood scores. This is in contrast to more traditional fast matches where a likelihood score is used. In addition, streaming SIMD extensions (SSE) for Intel machine instructions are used for fast Gaussian calculation. Results on a 20K Japanese broadcast news task show that the proposed fast match leads to about 30% improvement in speed with a slight performance degradation.