SPEECH-TO-TEXT CONVERSION IN FRENCH

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Abstract
Speech-to-text conversion of French necessitates that both the acoustic level recognition and language modeling be tailored to the French language. Work in this area was initiated at LIMSI over 10 years ago. In this paper a summary of the ongoing research in this direction is presented. Included are studies on distributional properties of French text materials; problems specific to speech-to-text conversion particular of French; studies in phoneme-to-grapheme conversion for continuous, error-free phonemic strings; past work on isolated-word speech-to-text conversion; and more recent work on continuous-speech, speech-to-text conversion. Also demonstrated is the use of phone recognition for both language and speaker identification. The continuous speech-to-text conversion for French is based on a speaker-independent, vocabulary-independent recognizer. In this paper phone recognition and word recognition results are reported evaluating this recognizer on read speech taken from the BREF corpus. The recognizer was trained on over 4 hours of speech from 57 speakers, and tested on sentences from an independent set of 19 speakers. A phone accuracy of 78.7% was obtained using a set of 35 phones. The word accuracy was 88% for a 1139 word lexicon and 86% for a 2716 word lexicon, with a word pair grammar with respective perplexities of 100 and 160. Using a bigram grammar, word accuracies of 85.5% and 81.7% were obtained with 5 K and 20 K word vocabularies, with respective perplexities of 122 and 205.

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