2 citations · 2 across the 5 of their papers we have counts for
5 papers
Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection
Phat Do, Matt Coler, Jelske Dijkstra +1
We compare using a PHOIBLE-based phone mapping method and using phonological features input in transfer learning for TTS in low-resource languages. We use diverse source languages…
The Effects of Input Type and Pronunciation Dictionary Usage in Transfer Learning for Low-Resource Text-to-Speech
Phat Do, Matt Coler, Jelske Dijkstra +1
We compare phone labels and articulatory features as input for cross-lingual transfer learning in text-to-speech (TTS) for low-resource languages (LRLs). Experiments with FastSpeec…
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…
Data-augmented cross-lingual synthesis in a teacher-student framework
Marcel de Korte, Jaebok Kim, Aki Kunikoshi +2
Cross-lingual synthesis can be defined as the task of letting a speaker generate fluent synthetic speech in another language. This is a challenging task, and resulting speech can s…
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…