A comparative study of continuous speech recognition using neural networks and hidden Markov models

The recognition performances of two front ends are compared for two continuous speech recognition tasks. First, a neural network model (NNM) front end was used, with frame labeling performed by a radial basis function network and segmentation by a Viterbi algorithm. The second front end was a discrete hidden Markov model (HMM), featuring explicit state duration probability distributions. Two experiments were performed. The first used a speaker-dependent database, with a lexicon of 571 words. Using a low-perplexity grammar, the NNM front end produced a word accuracy of 94% and a sentence accuracy of 86%. This was slightly inferior to the HMM front end, which produced word accuracies of 96% and sentence accuracies of 88%. Without a grammar, word accuracies of 58% (NNM) and 49% (HMM) were recorded. The second set of experiments used the MIT portion of the TIMIT database (415 speakers and 2072 sentences in total). Results were poor for both front ends, with the NNM producing marginally better results