Joint-sequence models for grapheme-to-phoneme conversion
نویسندگان
چکیده
منابع مشابه
Joint-sequence models for grapheme-to-phoneme conversion
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 selfcontained and detailed description of this method. We present a nove...
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Recently, neural sequence-to-sequence (Seq2Seq) models have been applied to the problem of grapheme-to-phoneme (G2P) conversion. These models offer a straightforward way of modeling the conversion by jointly learning the alignment and translation of input to output tokens in an end-to-end fashion. However, until now this approach did not show improved error rates on its own compared to traditio...
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In this work, we introduce several models for grapheme-tophoneme conversion: a conditional maximum entropy model, a joint maximum entropy n-gram model, and a joint maximum entropy n-gram model with syllabification. We examine the relative merits of conditional and joint models for this task, and find that joint models have many advantages. We show that the performance of our best model, the joi...
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ژورنال
عنوان ژورنال: Speech Communication
سال: 2008
ISSN: 0167-6393
DOI: 10.1016/j.specom.2008.01.002