Décodage avec ancrage pour la reconnaissance automatique de la parole
Daniel Moraru, Guillaume Gravier.

Automatic speech recognition mainly rely on hidden Markov models (HMM) which makes little use of phonetic knowledge. As an alternative, landmark based recognizers rely mainly on precise phonetic knowledge and exploit distinctive features. We propose a theoretical framework to combine both approaches by introducing prior (phonetic) knowledge in a non stationary HMM decoder. As a case study, we investigate how broad phonetic landmarks can be used to improve a HMM decoder by focusing the best path search. We show that every broad phonetic class bring a small improvement, the best improvement being obtained with glides. Using all broad phonetic classes brings a significant improvement by reducing the error rate from 22% to 14% on a broadcast news transcription task. We also experimentally demonstrate that landmarks does not need to be detected with precise boundaries and can be used to fasten the beam search algorithm.