Robust statistic modelling of systematic variabilities in continuous speech incorporating acoustic-articulatory relations

A system is described that takes advantage of the combination of properties of feature- and rule-based systems (evaluating systematic acoustic-articulatory dependencies) with properties of statistic-based methods (automatic training, uniform scoring). The main sources of variabilities in the acoustic speech signal, which are undoubtedly coarticulation and assimilation, are studied. Experimental results show that, by exploiting systematic acoustic-articulatory relations, it is possible to improve the performance of common pattern recognition methods. This is accomplished by introducing an articulatory feature vector in the acoustic-phonetic decoding scheme, as a feature level lying between the acoustic and phonemic level