The LIMSI continuous speech dictation system: evaluation on the ARPA Wall Street Journal task

Abstract

We report progress made at LIMSI in speaker-independent large vocabulary speech dictation using the ARPA Wall Street Journal-based CSR corpus. The recognizer makes use of continuous density HMM with Gaussian mixture for acoustic modeling and n-gram statistics estimated on the newspaper texts for language modeling. The recognizer uses a time-synchronous graph-search strategy which is shown to still be viable with vocabularies of up to 20 K words when used with bigram back-off language models. A second forward pass, which makes use of a word graph generated with the bigram, incorporates a trigram language model. Acoustic modeling uses cepstrum-based features, context-dependent phone models (intra and interword), phone duration models, and sex-dependent models. The recognizer has been evaluated in the Nov92 and Nov93 ARPA tests for vocabularies of up to 20,000 words. >