Can syllabification improve pronunciation by analogy of English?

Marchand, Yannick; Damper, R. I.

doi:10.1017/s1351324905004043

Cited by 25 publications

(12 citation statements)

References 28 publications

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“…Syllabification by analogy syllabifies words in either the orthographic or pronunciation domain. It closely follows the principles of pronunciation by analogy (PbA) set out in detail in our earlier publications (Damper & Eastmond, 1997;Marchand & Damper, 2000;Damper & Marchand, 2006;Marchand & Damper, 2007).…”

Section: Data-driven Methodsmentioning

confidence: 96%

“…When an unknown word is presented as an input to the system, so-called full pattern matching between the input letter string and database In our previous syllabification work using analogy (Marchand & Damper, 2007), we obtained best results by combining only 3 of the 5 scoring strategies when choosing between tied shortest paths. These were the product of arc frequencies, the frequency of the same pronunciation, and the 'weak link' (see Marchand &Damper, 2000 andDamper &Marchand, 2006 for full specification).…”

Section: Data-driven Methodsmentioning

confidence: 97%

“…The overall system uses a hand-crafted rule-based system (see Müller, 2001) that produces (all possible) candidate pronunciations; these are then ranked by the probabilistic syllable model and the most probable analysis selected to give the predicted pronunciation. Marchand and Damper (2007) describe a lazy learning procedure called syllabification by analogy (SbA) in which 78.1% of the approximately 20,000 words in Webster's Pocket Dictionary are correctly syllabified, but they do not assess how this performance compares to other approaches. This paper compares the performance of several variants of the two basic approaches to automatic syllabification: rule-based and data-driven.…”

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confidence: 99%

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Automatic Syllabification in English: A Comparison of Different Algorithms

2009

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Automatic syllabification of words is challenging, not least because the syllable is not easy to define precisely. Consequently, no accepted standard algorithm for automatic syllabification exists. There are two broad approaches: rule-based and data-driven. The rule-based method effectively embodies some theoretical position regarding the syllable, whereas the data-driven paradigm tries to infer “new” syllabifications from examples assumed to be correctly syllabified already. This article compares the performance of several variants of the two basic approaches. Given the problems of definition, it is difficult to determine a correct syllabification in all cases and so to establish the quality of the “gold standard” corpus used either to evaluate quantitatively the output of an automatic algorithm or as the example-set on which data-driven methods crucially depend. Thus, we look for consensus in the entries in multiple lexical databases of pre-syllabified words. In this work, we have used two independent lexicons, and extracted from them the same 18,016 words with their corresponding (possibly different) syllabifications. We have also created a third lexicon corresponding to the 13,594 words that share the same syllabifications in these two sources. As well as two rule-based approaches (Hammond's and Fisher's implementation of Kahn's), three data-driven techniques are evaluated: a look-up procedure, an exemplar-based generalization technique, and syllabification by analogy (SbA). The results on the three databases show consistent and robust patterns. First, the data-driven techniques outperform the rule-based systems in word and juncture accuracies by a very significant margin but require training data and are slower. Second, syllabification in the pronunciation domain is easier than in the spelling domain. Finally, best results are consistently obtained with SbA.

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Section: Data-driven Methodsmentioning

confidence: 96%

Section: Data-driven Methodsmentioning

confidence: 97%

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confidence: 99%

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Automatic Syllabification in English: A Comparison of Different Algorithms

2009

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“…PbA exploits the phonological knowledge inherent in a dictionary of words and their corresponding pronunciations. Since we have previously given a full description of Pronounce and our modifications to it elsewhere in this journal (Marchand and Damper 2007), an abbreviated specification follows. A seminal and still typical PbA program is Pronounce by Dedina and Nusbaum (1991), hereafter D&N, which forms the basis for our own PbA algorithm.…”

Section: Principles Of Pronunciation By Analogymentioning

confidence: 99%

Multilingual pronunciation by analogy

Soonklang¹,

Damper²,

Marchand

2008

Nat. Lang. Eng.

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Automatic pronunciation of unknown words (i.e., those not in the system dictionary) is a difficult problem in text-to-speech (TTS) synthesis. Currently, many data-driven approaches have been applied to the problem, as a backup strategy for those cases where dictionary matching fails. The difficulty of the problem depends on the complexity of spelling-to-sound mappings according to the particular writing system of the language. Hence, the degree of success achieved varies widely across languages but also across dictionaries, even for the same language with the same method. Further, the sizes of the training and test sets are an important consideration in data-driven approaches. In this paper, we study the variation of letter-to-phoneme transcription accuracy across seven European languages with twelve different lexicons. We also study the relationship between the size of dictionary and the accuracy obtained. The largest dictionaries of each language have been partitioned into ten approximately equal-sized subsets and combined to give ten different-sized test sets. In view of its superior performance in previous work, the transcription method used is pronunciation by analogy (PbA). Best results are obtained for Spanish, generally believed to have a very regular ('shallow') orthography, and poorest results for English, a language whose irregular spelling system is legendary. For those languages for which multiple dictionaries were available (i.e., French and English), results were found to vary across dictionaries. For the relationship between dictionary size and transcription accuracy, we find that as dictionary size grows, so performance grows monotonically. However, the performance gain decelerates (tends to saturate) as the dictionary increases in size; the relation can simply be described by a logarithmic regression, one parameter of which (α) can be taken as quantifying the depth of orthography of a language. We find that α for a language is significantly correlated with transcription performance on a small dictionary (approximately 10,000 words) for that language, but less so for asymptotic performance. This may be because our measure of asymptotic performance is unreliable, being extrapolated from the fitted logarithmic regression.

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“…Because of the productive nature of language, a dictionary look-up process for syllabification is inadequate. Rule-based systems are generally outperformed on out-ofdictionary words by data-driven methods, such as those of Daelemans et al (1997), Demberg (2006), Marchand and Damper (2007), and Trogkanis and Elkan (2010). Morphological segmentation is the task of dividing words into morphemes, the smallest meaning-bearing units in the word (Goldsmith, 2001).…”

Section: Introductionmentioning

confidence: 99%