Multi-layer perceptrons and probabilistic neural networks for phoneme recognition
Kjell O. E. Elenius, Hans G. C. Tråvén · 1993
Two artificial neural networks have been trained to recognise phonemes in continuous speech: multi-layer perceptron (MLP) nets and probabilistic neural networks (PNN). The speech material was recorded by one male Swedish speaker and the sentences were phonetically labelled. Fifty sentences were used for training and another fifty were used for testing. Both networks had a single hidden layer and 38 output nodes corresponding to Swedish phonemes. The MLP was trained by the supervised backpropagation algorithm. The PNN was trained by a selforganising clustering algorithm, a stochastic approximation to the expectation maximisation algorithm. The classification results for a feed-forward MLP and the PNN were rather similar, but an MLP with simple recurrency using context nodes gave the best performance. Several other differences of practical value was noted.