# End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning

arXiv: https://arxiv.org/abs/1904.06508 · HTML: https://mlsanity.com/paper/1904.06508/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2019.

## Built on by (most cited first, 30 of 30)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Towards Building Text-To-Speech Systems for the Next Billion Users](https://mlsanity.com/paper/2211.09536/) (2022)
- [Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features](https://mlsanity.com/paper/2203.03191/) (2022)
- [Low-Resource Multilingual and Zero-Shot Multispeaker TTS](https://mlsanity.com/paper/2210.12223/) (2022)
- [Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech](https://mlsanity.com/paper/2106.12896/) (2021)
- [Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis](https://mlsanity.com/paper/2103.03541/) (2021)
- [KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis Dataset](https://mlsanity.com/paper/2104.08459/) (2021)
- [KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics](https://mlsanity.com/paper/2201.05771/) (2022)
- [Meta Learning Text-to-Speech Synthesis in over 7000 Languages](https://mlsanity.com/paper/2406.06403/) (2024)
- [Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module](https://mlsanity.com/paper/2202.08164/) (2022)
- [Improved Meta Learning for Low Resource Speech Recognition](https://mlsanity.com/paper/2205.06182/) (2022)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [Distribution augmentation for low-resource expressive text-to-speech](https://mlsanity.com/paper/2202.06409/) (2022)
- [Unify and Conquer: How Phonetic Feature Representation Affects Polyglot Text-To-Speech (TTS)](https://mlsanity.com/paper/2207.01547/) (2022)
- [Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study](https://mlsanity.com/paper/2301.09099/) (2023)
- [Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning](https://mlsanity.com/paper/2403.06725/) (2024)
- [A multilingual training strategy for low resource Text to Speech](https://mlsanity.com/paper/2409.01217/) (2024)
- [Zero-Shot Text-to-Speech for Vietnamese](https://mlsanity.com/paper/2506.01322/) (2025)
- [Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis](https://mlsanity.com/paper/2110.04482/) (2021)
- [MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline](https://mlsanity.com/paper/2209.10848/) (2022)
- [Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration](https://mlsanity.com/paper/2305.15749/) (2023)
- [Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection](https://mlsanity.com/paper/2306.12040/) (2023)
- [Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding](https://mlsanity.com/paper/2206.15427/) (2022)
- [Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations](https://mlsanity.com/paper/2210.15131/) (2022)
- [Text2Data: Low-Resource Data Generation with Textual Control](https://mlsanity.com/paper/2402.10941/) (2024)
- [Align2Speak: Improving TTS for Low Resource Languages via ASR-Guided Online Preference Optimization](https://mlsanity.com/paper/2509.21718/) (2025)
- [The Effects of Input Type and Pronunciation Dictionary Usage in Transfer Learning for Low-Resource Text-to-Speech](https://mlsanity.com/paper/2306.00535/) (2023)
- [Maximizing Data Efficiency for Cross-Lingual TTS Adaptation by Self-Supervised Representation Mixing and Embedding Initialization](https://mlsanity.com/paper/2402.01692/) (2024)
- [LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models](https://mlsanity.com/paper/2411.13453/) (2024)
- [LuxEmo: Expressive Text-to-Speech Corpus for Luxembourgish](https://mlsanity.com/paper/2606.31947/) (2026)

## Abstract (arXiv)

End-to-end text-to-speech (TTS) has shown great success on large quantities of paired text plus speech data. However, laborious data collection remains difficult for at least 95% of the languages over the world, which hinders the development of TTS in different languages. In this paper, we aim to build TTS systems for such low-resource (target) languages where only very limited paired data are available. We show such TTS can be effectively constructed by transferring knowledge from a high-resource (source) language. Since the model trained on source language cannot be directly applied to target language due to input space mismatch, we propose a method to learn a mapping between source and target linguistic symbols. Benefiting from this learned mapping, pronunciation information can be preserved throughout the transferring procedure. Preliminary experiments show that we only need around 15 minutes of paired data to obtain a relatively good TTS system. Furthermore, analytic studies demonstrated that the automatically discovered mapping correlate well with the phonetic expertise.