First experiments on an HMM based double layer framework for automatic continuous speech recognition

Abstract

The usual approach to automatic continuous speech recognition is what can be called the acoustic-phonetic modelling approach. In this approach, voice is considered to hold two different kinds of information acoustic and phonetic . Acoustic information is represented by some kind of feature extraction out of the voice signal, and phonetic information is extracted from the vocabulary of the task by means of a lexicon or some other procedure. The main assumption in this approach is that models can be constructed that capture the correlation existing between both kinds of information. The main limitation of acoustic-phonetic modelling in speech recognition is its poor treatment of the variability present both in the phonetic level and the acoustic one. In this paper, we propose the use of a slightly modified framework where the usual acoustic-phonetic modelling is divided into two different layers: one closer to the voice signal, and the other closer to the phonetics of the sentence. By doing so we expect an improvement of the modelling accuracy, as well as a better management of acoustic and phonetic variability. Experiments carried out so far, using a very simpli ed version of the proposed framework, show a signi cant improvement in the recognition of a large vocabulary continuous speech task, and represent a promising start point for future research.