Improvement in N-best search for continuous speech recognition

Several techniques for reducing the search complexity of team search for continuous speech recognition task are proposed. Six heuristic methods for pruning are described and the parameters of the pruning am adjusted to keep constant the word error rate while reducing the computational complexity and memory demand. The evaluation of the effect of each pruning method is performed in the mixture stochastic trajectory model (MSTM). MSTM is a segment-based model using phonemes as the speech units. The set of tests in a speaker-dependent continuous speech recognition task shows that using the pruning methods, a substantial reduction of 67% of search effort is obtained in term of number of hypothesised phonemes during the search. All proposed techniques are independent of the acoustic models and therefore are applicable to other acoustic modeling techniques