The use of the Dempster-Shafer rule in the lexical component of a man-machine oral dialogue system

Romary, Laurent; Pierrel, Jean-Marie

doi:10.1016/0167-6393(89)90042-3

Cited by 1 publication

(1 citation statement)

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“…Clearly, the above characterization is very wide ranging. Consequently, fusion has been applied to a wide variety of pattern recognition and decision theoretic problems-using a plethora of theories, techniques, and tools--including some applications in computational linguistics (e.g., Brill and Wu 1998;van Halteren, Zavrel, and Daelemans 1998) and speech technology (e.g., Bowles and Damper 1989;Romary and Pierrel 1989). According to Abbott (1999, 290), "While the reasons [that] combining models works so well are not rigorously understood, there is ample evidence that improvements over single models are typical .... A strong case can be made for combining models across algorithm families as a means of providing uncorrelated output estimates."…”

Section: Information Fusion In Computational Linguisticsmentioning

confidence: 99%

A Multistrategy Approach to Improving Pronunciation by Analogy

Marchand

Damper

2000

Computational Linguistics

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Pronunciation by analogy (PbA) is a data-driven method for relating letters to sound, with potential application to next-generation text-to-speech systems. This paper extends previous work on PbA in several directions. First, we have included “full” pattern matching between input letter string and dictionary entries, as well as including lexical stress in letter-to-phoneme conversion. Second, we have extended the method to phoneme-to-letter conversion. Third, and most important, we have experimented with multiple, different strategies for scoring the candidate pronunciations. Individual scores for each strategy are obtained on the basis of rank and either multiplied or summed to produce a final, overall score. Five strategies have been studied and results obtained from all 31 possible combinations. The two combination methods perform comparably, with the product rule only very marginally superior to the sum rule. Nonparametric statistical analysis reveals that performance improves as more strategies are included in the combination: this trend is very highly significant (p < 0:0005). Accordingly for letter-to-phoneme conversion, best results are obtained when all five strategies are combined: word accuracy is raised to 65.5% relative to 61.7% for our best previous result and 63.0% for the best-performing single strategy. These improvements are very highly significant (p ∼ 0 and p < 0:00011 respectively). Similar results were found for phoneme-to-letter and letter-to-stress conversion, although the former was an easier problem for PbA than letter-to-phoneme conversion and the latter was harder. The main sources of error for the multistrategy approach are very similar to those for the best single strategy, and mostly involve vowel letters and phonemes.

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Section: Information Fusion In Computational Linguisticsmentioning

confidence: 99%