We present an automatic approach to learning criteria for classifying the parts-of-speech used in lexical mappings. This will further automate our knowledge acquisition system for non-technical users. The criteria for the speech parts are based on the types of the denoted terms along with morphological and corpus-based clues. Associations among these and the parts-of-speech are learned using th...

Motivated by the success of free-parts based representations in face recognition [1] we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent automatic speech reading. Hitherto, a major problem with canonical area-based approaches in automatic speech reading is the intrinsic lack of training observations due to the visual sp...

Motivated by the success of free-parts based representations in face recognition [1] we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent automatic speech reading. Hitherto, a major problem with canonical area-based approaches in automatic speech reading is the intrinsic lack of training observations due to the visual sp...

The Problem: Words in natural language sentences exhibit regularities in where they can appear in a sentence. These regularities are captured as parts of speech: nouns name things in the world; verbs represent actions and events; adjectives and adverbs describe properties of nouns and manners of verbs. Natural language has tremendous freedom in sharing meaning between these parts of speech (con...