Abstract
For multilingual text-to-speech synthesis, it is desirable to have reliable grapheme-to-phoneme conversion algorithms which can be easily adapted to different languages. I propose a flexible dual-route neural network algorithm which consists of two components: a constructor net for exploiting regularities of the mapping from graphemes to phonemes and a self-organizing map (SOM) for storing exceptions which are not captured by the constructor net. The SOM transcribes one word at a time, the constructor net one phoneme at a time. The constructor net output is then classified by mapping it onto a set of codebook vectors generated by Learning Vector Quantisation which capture the net's concept of each phoneme.
Thanks to T. Mark Ellison, Joachim Buhmann, Paul Taylor, and David Willshaw for valuable comments. The financial support of the Studienstiftung des deutschen Volkes and of ERASMUS programme ICP 95 NL 1186 is gratefully acknowledged.
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Wolters, M. (1996). A dual route neural net approach to grapheme-to-phoneme conversion. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_42
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DOI: https://doi.org/10.1007/3-540-61510-5_42
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