# Cross-Lingual Machine Speech Chain for Javanese, Sundanese, Balinese, and Bataks Speech Recognition and Synthesis

arXiv: https://arxiv.org/abs/2011.02128 · HTML: https://mlsanity.com/paper/2011.02128/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- [IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation](https://mlsanity.com/paper/2104.08200/) (2021)
- [NusaCrowd: Open Source Initiative for Indonesian NLP Resources](https://mlsanity.com/paper/2212.09648/) (2022)
- [Constructing and Expanding Low-Resource and Underrepresented Parallel Datasets for Indonesian Local Languages](https://mlsanity.com/paper/2404.01009/) (2024)
- [What Do Indonesians Really Need from Language Technology? A Nationwide Survey](https://mlsanity.com/paper/2506.07506/) (2025)
- [Location-based Twitter Filtering for the Creation of Low-Resource Language Datasets in Indonesian Local Languages](https://mlsanity.com/paper/2206.07238/) (2022)
- [Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay](https://mlsanity.com/paper/2606.11786/) (2026)
- [CultureTalk-ID: A Multi-Task Dialogue Benchmark for Cultural Commonsense in Indonesian Local Languages](https://mlsanity.com/paper/2607.21016/) (2026)

## Abstract (arXiv)

Even though over seven hundred ethnic languages are spoken in Indonesia, the available technology remains limited that could support communication within indigenous communities as well as with people outside the villages. As a result, indigenous communities still face isolation due to cultural barriers; languages continue to disappear. To accelerate communication, speech-to-speech translation (S2ST) technology is one approach that can overcome language barriers. However, S2ST systems require machine translation (MT), speech recognition (ASR), and synthesis (TTS) that rely heavily on supervised training and a broad set of language resources that can be difficult to collect from ethnic communities. Recently, a machine speech chain mechanism was proposed to enable ASR and TTS to assist each other in semi-supervised learning. The framework was initially implemented only for monolingual languages. In this study, we focus on developing speech recognition and synthesis for these Indonesian ethnic languages: Javanese, Sundanese, Balinese, and Bataks. We first separately train ASR and TTS of standard Indonesian in supervised training. We then develop ASR and TTS of ethnic languages by utilizing Indonesian ASR and TTS in a cross-lingual machine speech chain framework with only text or only speech data removing the need for paired speech-text data of those ethnic languages.