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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/3851

Title: Comparing manually-developed and data-driven rules for P2P learning
Authors: Loots, L
Davel, M
Barnard, E
Niesler, T
Keywords: Phoneme-to-phoneme learning
P2P
Pronunciation prediction
Pronunciation conversion
Grapheme-to-phoneme
G2P
PRASA 2009
Issue Date: Nov-2009
Publisher: PRASA 2009
Citation: Loots, L, Davel, M et al. 2009. Comparing manually-developed and data-driven rules for P2P learning. 20th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA). Stellenbosch, South Africa, 30 November - 01 December 2009, pp 35-40
Abstract: Phoneme-to-phoneme (P2P) learning provides a mechanism for predicting the pronunciation of a word based on its pronunciation in a different accent, dialect or language. The authors evaluate the effectiveness of manually-developed as well as automatically derived P2P rules for British to South African English pronunciation conversion. Using the freely-available Oxford Advanced Learners Dictionary of Contemporary English (OALD) as source, the two approaches to P2P conversion are compared to a manually-developed South African English pronunciation dictionary. The authors show that, when the British English pronunciation is known, a small manually-derived rule set is able to approximate the South African pronunciation surprisingly well. Furthermore they demonstrate that the best performance is achieved by data-driven P2P learning, which proves to be a better mechanism for pronunciation prediction than both manually-derived P2P rules as well as data-driven grapheme-to-phoneme (G2P) conversion.
Description: 20th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA). Stellenbosch, South Africa, 30 November - 01 December 2009
URI: http://hdl.handle.net/10204/3851
Appears in Collections:Human language technologies
Mobile intelligent autonomous systems
General science, engineering & technology

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