Adaptive dialog based upon multimodal language acquisition

Communicating by voice with speech-enabled computer applications based on preprogrammed rule grammars suffers from constrained vocabulary and sentence structures. Deviations from the allowed language result in an unrecognized utterance that will not be understood and processed by the system. One way to alleviate this restriction consists in allowing the user to expand the computer´s recognized and understood language by teaching the computer system new language knowledge. We present an adaptive dialog system capable of learning from users new words, phrases and sentences, and their corresponding meanings. User input incorporates multiple modalities, including speaking, typing, pointing, drawing and image capturing. The allowed language can thus be expanded in real time by users according to their preferences. By acquiring new language knowledge the system becomes more capable in specific tasks, although its language is still constrained.