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A Comparison of Data-Derived and Knowledge-Based Modeling of Pronunciation Variation

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wester.2000.2 ICSLP.pdf (55.43Kb)
Date
10/2000
Author
Wester, Mirjam
Fosler-Lussier, Eric
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Abstract
This paper focuses on modeling pronunciation variation in two different ways: data-derived and knowledge-based. The knowledge-based approach consists of using phonological rules to generate variants. The data-derived approach consists of performing phone recognition, followed by various pruning and smoothing methods to alleviate some of the errors in the phone recognition. Using phonological rules led to a small improvement in WER; whereas, using a data-derived approach in which the phone recognition was smoothed using simple decision trees (d-trees) prior to lexicon generation led to a significant improvement compared to the baseline. Furthermore, we found that 10% of variants generated by the phonological rules were also found using phone recognition, and this increased to 23% when the phone recognition output was smoothed by using d-trees. In addition, we propose a metric to measure confusability in the lexicon and we found that employing this confusion metric to prune variants results in roughly the same improvement as using the d-tree method.
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http://www.isca-speech.org/archive/icslp_2000

http://hdl.handle.net/1842/987
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