Probabilistic grammar for phonetic to French transcription

In this paper, we study the combination of an information theoretic tool (Markov modeling of natural language [3]) with probabilistic grammatical analysis. Continuous Speech Recognition for natural language raises a lot of difficulties, both for the acoustic processing and the linguistic decoding. Our work specifically concerns the linguistic decoding techniques for a very large (140,000 entries) French dictionary, and a oral open discourse. So the task is to transcribe a continuous string of pseudo-phonemes into written text. This string would be ideally the output of a perfect acoustic processor. We present a grammar designed for automatic transcription and compute probabilities for the rules. We compare its results with those obtained earlier with Markov modeling. We show that it is possible to combine the two approaches and get better results than each model separately.