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Abstract : |
It is traditionally assumed that various sources of linguistic knowledge and their in-teraction should be formalised in order to be able to convert words into their phone-mic representations with reasonable accu-racy. We show that using supervised learn-ing techniques, based on a corpus of tran-scribed words, the same and even better performance can be achieved, without ex-plicit modeling of linguistic knowledge. In this paper we present two instances of this approach. A first model implements a variant of instance-based learning, in which a weighed similarity metric and a database of prototypical exemplars are used to pre-dict new mappings. In the second model, grapheme-to-phoneme mappings are looked up in a compressed text-to-speech lexicon (table lookup) enriched with default map-pings. We compare performance and accu-racy of these approaches to a connectionist (backpropagation) approach and to the lin-guistic knowledge-based approach. 1, |
