Phonetic classification and recognition using HMM representation of overlapping articulatory features for all classes of English sounds

Abstract

Our efforts in developing a feature-based general statistical framework intended for unlimited-vocabulary speech recognition are reported. The design of the feature-based atomic units of speech is aimed at parsimonious scheme to share the inter-word and inter-phone speech data. Our basic design philosophy has been motivated by the theory of distinctive features and by a new form of phonology which argues for the use of multidimensional articulatory structures. The work reported is a significant extension of our earlier studies (see Deng and Erler (1992) and Deng, Lenning and Mermelstein (1990)) in three aspects. First, a comprehensive set of features is developed, enabling the recognisor to operate on all classes of English sound. Second, a more efficient strategy is derived for feature-based lexical representation. Third, more extensive evaluation results, including both the phonetic classification and phonetic recognition results, are reported. >