Probabilistic context-free grammars for phonology

Müller, Karin

doi:10.3115/1118647.1118655

Cited by 7 publications

(12 citation statements)

References 11 publications

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“…Krenn (1997) obtains tag accuracy of 98.34%, compared to our system's tag accuracy of 99.97% when trained on 250K words. With a hand-crafted grammar, Müller (2002) achieves 96.88% word accuracy on CELEX-derived syllabifications, with a training corpus of two million tokens. Without a handcrafted grammar, she reports 90.45% word accuracy (Müller, 2006).…”

Section: Other Languagesmentioning

confidence: 99%

On the syllabification of phonemes

Bartlett

Kondrak

Cherry

2009

Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Com

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Syllables play an important role in speech synthesis and recognition. We present several different approaches to the syllabification of phonemes. We investigate approaches based on linguistic theories of syllabification, as well as a discriminative learning technique that combines Support Vector Machine and Hidden Markov Model technologies. Our experiments on English, Dutch and German demonstrate that our transparent implementation of the sonority sequencing principle is more accurate than previous implementations, and that our language-independent SVM-based approach advances the current state-of-the-art, achieving word accuracy of over 98% in English and 99% in German and Dutch.

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Section: Other Languagesmentioning

confidence: 99%

On the syllabification of phonemes

Bartlett

Kondrak

Cherry

2009

Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Com

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“…In addition, probabilistic context-free grammars have been used as language models for speech recognition tasks [13], [14]. They have also been used for syllabification tasks [15], [16].…”

Section: Related Workmentioning

confidence: 99%

Using a Probabilistic Syllable Model to Improve Scene Text Recognition

Feild

Learned-Miller

Smith

2013

2013 12th International Conference on Document Analysis and Recognition

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Abstract-This paper presents a new language model for text recognition in natural images. Many existing techniques incorporate n-gram information as an additional source of information. One problem is that some n-grams are very uncommon, but will still appear in a word across a syllable boundary. These words are given a low probability under an n-gram model. To overcome this problem, we introduce a probabilistic syllable model that uses a probabilistic context-free grammar to generate recognized word labels that are consistent with syllables. In other words, labels generated by this model are pronounceable. This is important for scene text recognition where text often includes proper nouns and standard dictionary information cannot be a useful resource. We show that this language model leads to increased recognition accuracy over a bigram model and discuss the benefits over a dictionary model.

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“…In this section, we report on our experiments with four different phonotactic grammars introduced in Section 2.1 (see grammar 2.1.3-2.1.6), as well as with a re-implementation of Müller's less complex grammar (Müller, 2002). All these grammars are trained on a corpus of transcribed words from the pronunciation lexicon CELEX.…”

Section: N 3 Experimentsmentioning

confidence: 99%

Improving syllabification models with phonotactic knowledge

Amsterdam

2006

Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology - SIGPHON '06

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We report on a series of experiments with probabilistic context-free grammars predicting English and German syllable structure. The treebank-trained grammars are evaluated on a syllabification task. The grammar used by Müller (2002) serves as point of comparison. As she evaluates the grammar only for German, we reimplement the grammar and experiment with additional phonotactic features. Using bi-grams within the syllable, we can model the dependency from the previous consonant in the onset and coda. A 10fold cross validation procedure shows that syllabification can be improved by incorporating this type of phonotactic knowledge. Compared to the grammar of Müller (2002), syllable boundary accuracy increases from 95.8% to 97.2% for English, and from 95.9% to 97.2% for German. Moreover, our experiments with different syllable structures point out that there are dependencies between the onset on the nucleus for German but not for English. The analysis of one of our phonotactic grammars shows that interesting phonotactic constraints are learned. For instance, unvoiced consonants are the most likely first consonants and liquids and glides are preferred as second consonants in two-consonantal onsets.

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Probabilistic context-free grammars for phonology

Cited by 7 publications

References 11 publications

On the syllabification of phonemes

On the syllabification of phonemes

Using a Probabilistic Syllable Model to Improve Scene Text Recognition

Improving syllabification models with phonotactic knowledge

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