natural-language-processing-3

Polyphone disambiguation

3260 papers • 126 benchmarks • 313 datasets

A part of the TTS-front end framework which serves to predict the correct pronunciation for the input polyphone characters.

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Benchmarks

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Use these libraries to find polyphone-disambiguation-3 models and implementations

Datasets

CPP

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Most implemented papers

Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech

Yi Ren, Jinglin Liu, Ziyue Jiang, Zhou Zhao, Zhe Su, Qian Yang, Zhe Ye•Sat Jun 04 2022

Dict-TTS is proposed, a semantic-aware generative text-to-speech model with an online website dictionary that outperforms several strong baseline models in terms of pronunciation accuracy and improves the prosody modeling of TTS systems.

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g2pW: A Conditional Weighted Softmax BERT for Polyphone Disambiguation in Mandarin

Yi-Chang Chen, Yu-Chuan Chang, Yenling Chang, Yi-Ren Yeh•Sat Mar 19 2022

This work proposes a novel approach, called g2pW, which adapts learnable softmax-weights to condition the outputs of BERT with the polyphonic character of interest and its POS tagging and shows that it outperforms existing methods on the public CPP dataset.

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