Volker Steinbiss

Large vocabulary continuous speech recognition of Wall Street Journal data

We report on recent developments of the Philips large vocabulary speech recognition system and on our experiments with the Wall Street Journal (WSJ) corpus. A two-pass decoding has been devised that allows an easy integration of more complex language models. First, a word lattice is produced using a time synchronous beam search with a bigram language model. Next, a higher-order language model is applied to the lattice at the phrase level. The conditions insuring the validity of this approach are explained and practical results for trigram demonstrate its usefulness. The main system development stages on WSJ data are presented and our final recognizers are evaluated on Nov. 92 and Nov. 93 test-data for both 5 K and 20 K vocabularies.<<ETX>>

AN OVERVIEW OF THE PHILIPS RESEARCH SYSTEM FOR LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION

This paper gives an overview of a research system for phoneme based, large vocabulary continuous speech recognition. The system to be described has been applied to the SPICOS task, the DARPA RM task and a 12000 word dictation task. Experimental results for these three tasks will be presented. Like many other systems, the recognition architecture is based on an integrated statistical approach. In this paper, we describe the characteristic features of the system as opposed to other systems: (1) The Viterbi criterion is consistently applied both in training and testing. (2) Continuous mixture densities are used without any tying or smoothing; this approach can be viewed as a sort of ‘statistical template matching’. (3) Time-synchronous beam search is used consistently throughout all tasks; extensions using a tree organization of the vocabulary and phoneme lookahead are presented so that a 12000 word task can be handled.