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Grapheme-to-phoneme conversion and its application to transliteration Open Access


Other title
Grapheme to phoneme
Natural language processing
Computational linguistics
Text to speech
String transduction
Speech synthesis
Online large margin training
Type of item
Degree grantor
University of Alberta
Author or creator
Jiampojamarn, Sittichai
Supervisor and department
Grzegorz Kondrak (Computing Science)
Examining committee member and department
Harald Baayen (Linguistics)
Anoop Sarkar (School of Computing Science, Simon Fraser University)
Randy Goebel (Computing Science)
Dale Schuurmans (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Grapheme-to-phoneme conversion (G2P) is the task of converting a word, represented by a sequence of graphemes, to its pronunciation, represented by a sequence of phonemes. The G2P task plays a crucial role in speech synthesis systems, and is an important part of other applications, including spelling correction and speech-to-speech machine translation. G2P conversion is a complex task, for which a number of diverse solutions have been proposed. In general, the problem is challenging because the source string does not unambiguously specify the target representation. In addition, the training data include only example word pairs without the structural information of subword alignments. In this thesis, I introduce several novel approaches for G2P conversion. My contributions can be categorized into (1) new alignment models and (2) new output generation models. With respect to alignment models, I present techniques including many-to-many alignment, phonetic-based alignment, alignment by integer linear programing and alignment-by-aggregation. Many-to-many alignment is designed to replace the one-to-one alignment that has been used almost exclusively in the past. The new many-to-many alignments are more precise and accurate in expressing grapheme-phoneme relationships. The other proposed alignment approaches attempt to advance the training method beyond the use of Expectation-Maximization (EM). With respect to generation models, I first describe a framework for integrating many-to-many alignments and language models for grapheme classification. I then propose joint processing for G2P using online discriminative training. I integrate a generative joint n-gram model into the discriminative framework. Finally, I apply the proposed G2P systems to name transliteration generation and mining tasks. Experiments show that the proposed system achieves state-of-the-art performance in both the G2P and name transliteration tasks.
License granted by Sittichai Jiampojamarn ( on 2010-12-07T23:43:17Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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