Joint-sequence models for grapheme-to-phoneme conversion Published on May 1, in Speech Communication 1. Maximilian Bisani 8 Estimated H-index: 8. Estimated H-index: Find in Lib.

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Published in Speech Commun. Maximilian Bisani , Hermann Ney. Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Grapheme-to-phoneme conversion is the task of finding the pronunciation of a word given its written form.

It has important applications in text-to-speech and speech recognition. Joint-sequence models are a simple and theoretically stringent probabilistic framework that is applicable to this problem. This article provides a self-contained and detailed description of this method.

We present a novel estimation algorithm and demonstrate high accuracy on a variety of databases. View via Publisher. Save to Library. Create Alert. Launch Research Feed. Share This Paper.

Figures, Tables, and Topics from this paper. Figures and Tables. Citations Publications citing this paper. Analysis of sequence to sequence neural networks on grapheme to phoneme conversion task Sivanand Achanta , Ayushi Pandey , Suryakanth V. Language identification for proper name pronunciation Oluwapelumi Giwa Computer Science Lexical and language modeling for Russian large vocabulary continuous speech recognition Sergey Zablotskiy Computer Science Grapheme-to-phoneme conversion in the era of globalization Tatyana Valerievna Polyakova Computer Science References Publications referenced by this paper.

Conditional and joint models for grapheme-to-phoneme conversion Stanley F. Grapheme-tophone using finite-state transducers. Caseiro , I. Trancoso , L. Oliveira , C. Viana Proc. Phonemic transcription by analogy in text-to-speech synthesis: Novel word pronunciation and lexicon compression Paul C. Bagshaw Computer Science Comput. Speech Lang. The Carnegie Mellon pronouncing dictionary.

Weide Kingsbury , S. Strassel , C. McLemore , R. LDC97L20 Related Papers. Table 3 Comparison of model initialization and training schemes. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy , Terms of Service , and Dataset License.


Incorporating syllabification points into a model of grapheme-to-phoneme conversion

We'd like to understand how you use our websites in order to improve them. Register your interest. A model to convert a grapheme into a phoneme G2P is crucial in the natural language processing area. In general, it is developed using a probabilistic-based data-driven approach and directly applied to a sequence of graphemes with no other information. Important research shows that incorporating information of syllabification point is capable of improving a probabilistic-based English G2P. However, the information should be accurately provided by a perfect orthographic syllabification. Some noises or errors of syllabification significantly reduce the G2P performance.


Joint-sequence models for grapheme-to-phoneme conversion

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