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eess.AS2023★ 2 cited
Resource-Efficient Fine-Tuning Strategies for Automatic MOS Prediction in Text-to-Speech for Low-Resource Languages
Phat Do, Matt Coler, Jelske Dijkstra +1
We train a MOS prediction model based on wav2vec 2.0 using the open-access data sets BVCC and SOMOS. Our test with neural TTS data in the low-resource language (LRL) West Frisian s…
eess.AS2020
Efficient neural speech synthesis for low-resource languages through multilingual modeling
Marcel de Korte, Jaebok Kim, Esther Klabbers
Recent advances in neural TTS have led to models that can produce high-quality synthetic speech. However, these models typically require large amounts of training data, which can m…