Learning phoneme recognition using neural networks

The authors have applied two neural-network models (back-propagation network and radial-basis-functions network) to a static speech recognition problem. The radial-basis-functions network offers training times of over two orders of magnitude faster than back-propagation, when training networks to similar power and generality. The authors have computed recognition statistics of the two models with varying numbers of hidden units on this recognition problem. The back-propagation network may offer increased generalization and robustness. Both models compare favorably with a vector-quantized hidden Markov model on the same problem.>