Novel-word pronunciation: a cross-language study
Speech Communication - Speech science and technology: a selection from the papers presented at the Fourth International Conference in Speech Science and Technology (SST-92)
An introduction to text-to-speech synthesis
An introduction to text-to-speech synthesis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Experimental evaluation of expert fusion strategies
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Pattern Recognition Letters
Can syllabification improve pronunciation by analogy of English?
Natural Language Engineering
Multilingual pronunciation by analogy
Natural Language Engineering
Computer Speech and Language
The effect of lexicon composition in pronunciation by analogy
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
A probabilistic approach to pronunciation by analogy
Computer Speech and Language
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Automatic pronunciation of words from their spelling alone is a hard computational problem, especially for languages like English and French where there is only a partially consistent mapping from letters to sound. Currently, the best known approach uses an inferential process of analogy with other words listed in a dictionary of spellings and corresponding pronunciations. However, the process produces multiple candidate pronunciations and little or no theory exists to guide the choice among them. Rather than committing to one specific heuristic scoring method, it may be preferable to use multiple strategies (i.e., soft experts) and then employ information fusion techniques to combine them to give a final result. In this paper, we compare four different fusion schemes, using three different dictionaries (with different codings for specifying the pronunciations) as the knowledge base for analogical reasoning. The four schemes are: fusion of raw scores; rank fusion using Borda counting; rank fusion using non-uniform values; and rank fusion using non-uniform values weighted by a measure of prior performance of the experts. All possible combinations of five different expert strategies are studied. Although all four fusion schemes outperformed the single best strategy, results show clear superiority of rank fusion over the other methods.