This paper describes NETtalk, a class of massively-parallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed human performance. (i) The learning follows a power law. (;i) The more words the network learns, the better it is at generalizing and correctly pronouncing new words, (iii) The performance of the network degrades very slowly as connections in the network are damaged: no single link or processing unit is essential. (iv) Relearning after damage is much faster than learning during the original training. (v) Distributed or spaced practice is more effective for long-term retention than massed practice. Network models can be constructed that have the same performance and learning characteristics on a particular task, but differ completely at the levels of synaptic strengths and single-unit responses. However, hierarchical clustering techniques applied to NETtalk reveal that these different networks have similar internal representations of letter-to-sound correspondences within groups of processing units. This suggests that invariant internal representations may be found in assemblies of neurons intermediate in size between highly localized and completely distributed representations.

Despite the fact that many symbolic and connectionist (neural net) learning algorithms are addressing the same problem of learning from classified examples, very little Is known regarding their comparative strengths and weaknesses. This paper presents the results of experiments comparing the ID3 symbolic learning algorithm with the perceptron and back-propagation connectionist learning algorithms on several large real-world data sets. The results show that ID3 and perceptron run significantly faster than does backpropagation, both during learning and during classification of novel examples. However, the probability of correctly classifying new examples is about the same for the three systems. On noisy data sets there is some indication that backpropagation classifies more accurately. 1.

Unrestricted English text can be converted to speech by applying phonological rules and handling exceptions with a look-up table. However, this approach is highly labor intensive since each entry and rule must be hand-crafted. NETtalk is an alternative approach that is based on an automated learning procedure for a parallel network of deterministic processing units. ~fter ' training on a corpus of informal continuous speech, it achieves good performance and generalizes to novel words. The distributed internal representations of the phonological regularities discovered by the network are damage resistant.

Unrestricted English text can be converted to speech by applying phonological rules and handling exceptions with a look-up table. However, this approach is highly labor intensive since each entry and rule must be hand-crafted. NETtalk is an alternative approach that is based on an automated learning procedure for a parallel network of deterministic processing units. After training on a corpus of informal continuous speech, it achieves good performance and generalizes to novel words. The distributed internal representations of the phonological regularities discovered by the network are damage resistant.