In a large vocabulary isolated word automatic speech recognition system, using Hidden Markov Models(HMM), it is time consuming to perform a detailed likelihood computation of an input utterance for all the words of the dictionary. A tree based A* search algorithm is presented for finding the N best word hypotheses, using a new likelihood estimator to drive time asyncronously the search. The proposed likelihood estimator has been tested with two different tasks: a large vocabulary (5000 words) multi-speaker radiological reports recognizer, and a 94 word speaker-independent command language recognizer. In both cases, the speed performance was improved by a factor of about 6, without losing admissibility. Using discrete models, the mean word hypothesis evaluation time is 8 ms on Sun4/330 workstation, the recognition rates vary from 94% to 98% depending on the speaker in the 5000 word task with a bigram language model, and 91% in the speaker-independent task.
Bibliographic reference. Antoniol, G. / Brugnara, F. / Giuliani, D. (1991): "Admissible strategies for acoustic matching with a large vocabulary", In EUROSPEECH-1991, 589-592.