A 10000-word continuous-speech recognition system

Some results obtained when the recognition vocabulary size of a phoneme-based speaker-dependent continuous-speech recognizer was increased from 1000 to 10000 words are reported. The potential search space increased from 46000 to 516000 states without problems for the data-driven search. Increasing the recognition vocabulary by a factor of 10 (from a perplexity of 917 to 9686) increased the word error rate by a factor of two (from 21.8% to 43.1%). Phoneme models were tested with both discrete probabilities and continuous mixture densities. The mixture density models performed better; moreover, they saved about half of the search costs. A language model was found to be very important for a larger vocabulary size. With a test set perplexity of 388 (i.e. a reduction by a factor of 25 compared to the case without a bigram model) the error rate decreased by a factor of 2.4. In order to check how meaningful perplexity is for the prediction of the system´s performance, a stochastic language model was constructed with a perplexity of 1000, the size of the vocabulary used in previous experiments, and about the same error rate was obtained