A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping

The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses and it was shown that BP consistently out-performs ID3 on this task by several percentage points.

Abstract

The performance of the error backpropagation BP and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses.Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points.Three hypotheses explaining this di erence were explored: a ID3 is over tting the training data, b BP is able to share hidden units across several output units and hence can learn the output units better, and c BP captures statistical information that ID3 does not.We conclude that only hypothesis c is correct.By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be approached but not matched.More complex statistical procedures can improve the performance of both BP and ID3 substantially.A study of the residual errors suggests that there is still substantial room for improvement in learning methods for text-to-speech mapping.