Toward Text-To-Speech in Low-Resource and Unwritten Languages by Leveraging Transfer Learning: Application in Viet Muong Closed Language Pair

  • Pham Van-Dong
Keywords: Computing methodologies → Speech Synthesis, Tacotron 2, low-resource languages, unwritten language, Muong speech, transfer learning


Text-to-speech systems require a lot of text and speech data to train models on. But with over 6,000 languages in the world, making text-to-speech systems for minority and low-resource languages is very difficult. Traditional text-to-speech has two parts: an acoustic model that predicts sounds from text and a vocoder that turns the sounds into waveforms. This paper proposes a text-to-speech system for languages with very little data to support minority languages. It uses three techniques: 1. Pre-training the acoustic model on languages with a lot of data, then fine-tuning on the low-resource language; 2. Using "knowledge distillation" to adapt the model to match a high-quality example voice; 3. Treating input text data for a minority language like Muong the same way as Vietnamese text data. We first learn linguistic features from Vietnamese speech data using a standard Tacotron 2 acoustic model. Then, we train the acoustic model on Muong speech data, starting from the weights of the Vietnamese model. The synthesized Muong speech has a naturalness score of 3.63 out of 5.0 and a Mel Cepstral Distortion of 5.133, based on 60 minutes of Muong data. These results show the effectiveness and quality of the Muong text-to-speech system, built with very little Muong language data.


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How to Cite
Van-Dong, P. (2024). Toward Text-To-Speech in Low-Resource and Unwritten Languages by Leveraging Transfer Learning: Application in Viet Muong Closed Language Pair. European Journal of Science, Innovation and Technology, 4(3), 14-28. Retrieved from