Towards a large-vocabulary French vocal dictation based on a size-independent language-model search using the INRS recognizer

Reports the progress of the large-vocabulary French-speech vocal dictation studies at INRS-Te´le´com. To evaluate such progress, the hidden Markov model (HMM) based recognizer of INRS is used. This recognizer, which represents each phone using HMMs, uses context-dependent phone modeling and n-gram statistics in order to cope with both coarticulation and phonological phenomena, respectively. A series of experiments on speaker-independent continuous-speech recognition have been carried out using a subset of the large read-speech French-language corpus, BREF, containing recordings of texts selected from the French newspaper Le Monde. We show through experiments that using a lexical graph that ignores the language model states and homophone distinctions and postponing the application of such knowledge to a post-processor simplifies the recognition process while keeping its high accuracy. The word recognition rate, using gender-dependent vector quantization (VQ) models, a 20,000-word pronunciation variants-based lexicon and a bigram model estimated using Le Monde text data, was found to be 91.62% for males and 90.98% for females