Model overview
XPhoneBERT is the first pre-trained multilingual model for phoneme representations for text-to-speech(TTS). XPhoneBERT has the same model architecture as BERT-base, trained using the RoBERTa pre-training approach on 330M phoneme-level sentences from nearly 100 languages and locales. Experimental results show that employing XPhoneBERT as an input phoneme encoder significantly boosts the performance of a strong neural TTS model in terms of naturalness and prosody and also helps produce fairly high-quality speech with limited training data. The general architecture and experimental results of XPhoneBERT can be found in our INTERSPEECH 2023 paper: @inproceedings{xphonebert, title = {{XPhoneBERT: A Pre-trained Multilingual Model for Phoneme Representations for Text-to-Speech}}, author = {Linh The Nguyen and Thinh Pham and Dat Quoc Nguyen}, booktitle = {Proceedings of the 24th Annual Conference of the International Speech Communication Association (INTERSPEECH)}, year = {2023}, pages = {5506--5510} }
Key features
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Public benchmarks & run configuration
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Sources & traceability
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