Syntactic neural networks for text-phonetics translation

It is shown how syntactic neural networks can be applied to the problem of translating orthographic strings to phonetics strings, and vice versa, due to the symmetry of the model. This is unusual in text-phonetics translation systems; most systems have to be trained to operate in a single direction. Another novel feature is the lack of supervision required during training. The only requirement is that one have whole-word orthographic/phonetic symbol-string pairs. To test the system the authors have formed a lexicon of (6000, single-syllable) such pairs in English by extracting the relevant information from the machine-readable Oxford Advanced Learner´s Dictionary. Results are presented for cases where the training set varies between 10 and 2000 words. In each case, the trained nets are tested on the training set and an equal-size (disjoint) test set. Present results are poor compared to conventional translation systems, but most of the errors may be due to the system´s immaturity