Lexical access to large vocabularies for speech recognition

A large-vocabulary isolated-word recognition system based on the hypothesize-and-test paradigm is described. Word preselection is achieved by segmenting and classifying the input signal in terms of broad phonetic classes. A lattice of phonetic segments is generated and organized as a graph. Word hypothesization is obtained by matching this graph against the models of all vocabulary words, where a word model is itself a phonetic representation made in terms of a graph. A modified dynamic programming matching procedure gives an efficient solution to this graph-to-graph matching problem. Hidden Markov models (HMMs) of subword units are used as a more detailed knowledge in the verification step. The word candidates generated by the previous step are represented as sequences of diphone-like subword units, and the Viterbi algorithm is used for evaluating their likelihood. Lexical knowledge is organized in a tree structure where the initial common subsequences of word descriptions are shared, and a beam-search strategy carries on the most promising paths only. The results show that a complexity reduction of about 73% can be achieved by using the two-pass approach with respect to the direct approach, while the recognition accuracy remains comparable