Training set design for connected speech recognition

Two methods for generating training sets for a speech recognition system are studied. The first uses a nondeterministic statistical method to generate a uniform distribution of sentences from a finite state machine (FSM) represented in digraph form. The second method, a deterministic heuristic approach, takes into consideration the importance of word ordering to address the problem of coarticulation effects. The two methods are critically compared. The first algorithm, referred to as MARKOV, converts the FSM into a first-order Markov model. The digraphs are determined, transitive closure computed, transition probabilities are assigned, and stopping criteria established. An efficient algorithm for computing these parameters is described. Statistical tests are conducted to verify performance and demonstrate its utility. A second algorithm for generating training sentences, referred to as BIGRAM, uses heuristics to satisfy three requirements: adequate coverage of basic speech (subword) units; adequate coverage of words in the recognition vocabulary (intraword contextual units); and adequate coverage of word pairs bigrams (interword contextual units).<
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