用类比法改进发音概率估计

J. Kujala, A. Nandi
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引用次数: 0

摘要

类比发音是一种基于匹配已知单词及其发音的子串为以前未见过的书面单词生成语音转录的方法。该方法固有地产生了几个候选发音,并提出了许多启发式方法来选择最佳发音。在[1]中,提出了一种理论上合理的概率方法来对发音进行评分,其性能与最佳启发式方法相当。然而,一个特定的修改——对子串发音的估计概率应用分数次幂——也被发现可以提高性能。在本文中,我们将解释这一意想不到的改进。我们表明分数次幂实际上提高了候选发音概率的估计。这也间接解释了b[2]中提出的当前最佳启发式算法的良好性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved estimation of probabilities in pronunciation by Analogy
Pronunciation by Analogy is a method for generating phonetic transcriptions for previously unseen written words based on matching substrings of known words and their pronunciations. The method inherently generates several candidate pronunciations and a multitude of heuristics have been proposed for choosing the best one. In [1], a theoretically justified probabilistic approach for scoring the pronunciations was proposed, with performance on par with the best heuristic methods. However, a certain ad hoc modification - a fractional power applied to the estimated probabilities of the substring pronunciations - was also found to improve performance. In this article, we give an explanation for this unexpected improvement. We show that the fractional power in fact improves the estimates of the candidate pronunciation probabilities. This also gives an indirect explanation of the good performance of the current best heuristic proposed in [2].
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