Grapheme to Phoneme Conversion: Using Input Strictly Local Finite State Transducers

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2019
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Haverford College. Department of Computer Science
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Thesis
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Award
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eng
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Open Access
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Abstract
This thesis explores the many methods of Grapheme to Phoneme Conversion (G2P) including dictionary look-up, rule-based approaches, and probabilistic approaches such as Joint Sequence Models (JSM), Recurrent Neural Networks (RNN), and weighted finite state automata (WFST) as well as a discussion of letter to phoneme alignments methods. We then explain Strictly Local languages and functions and their previous applications in an Input Strictly Local FST Learning Algorithm. Finally, I propose a further application for G2P conversion by adapting the Input Strictly Local FST Learning Algorithm. My results indicate that while this algorithm had some success learning G2P, future work will be necessary to improve accuracy by implementing a probabilistic model.
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