A word hypothesizer for a large vocabulary continuous speech understanding system

A continuous-speech recognition and understanding system for a thousand-word vocabulary has been designed and implemented. It is able to answer queries put to a geographical database in natural Italian language. A discussion is presented of the recognition component of the system. It can produce a word lattice that is then processed by a syntactic-semantic component. In addition, a linguistic decoder exploiting word-pair constraints has been investigated. Its results have been compared to those obtained by similar approaches reported in the literature. The system relies on word preselection through lexical access by means of broad phonetic classes and on hidden Markov modeling of subword units. The improvements to the basic approach are presented and system performance is given. Average word accuracy and correct sentence recognition obtained for speaker-dependent tests performed by two speakers pronouncing 214 sentences are 94.5% and 89.3%, respectively. The perplexity of the word-pair language model is 25