# Which Languages Transfer Best to Warlpiri? A Similarity-Based Study for Low-Resource ASR

arXiv: https://arxiv.org/abs/2607.10256 · HTML: https://mlsanity.com/paper/2607.10256/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 11 Jul 2026.

## Builds on (most cited first, 18 of 18)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [Exploiting Adapters for Cross-lingual Low-resource Speech Recognition](https://mlsanity.com/paper/2105.11905/) (2021)
- [A Balanced Data Approach for Evaluating Cross-Lingual Transfer: Mapping the Linguistic Blood Bank](https://mlsanity.com/paper/2205.04086/) (2022)
- [Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity](https://mlsanity.com/paper/2111.01326/) (2021)
- [Investigating the Impact of Cross-lingual Acoustic-Phonetic Similarities on Multilingual Speech Recognition](https://mlsanity.com/paper/2207.03390/) (2022)
- [Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis](https://mlsanity.com/paper/2501.06810/) (2025)
- [Whispering in Amharic: Fine-tuning Whisper for Low-resource Language](https://mlsanity.com/paper/2503.18485/) (2025)
- [Sonorant spectra and coarticulation distinguish speakers with different dialects](https://mlsanity.com/paper/2110.03756/) (2021)
- [A study of conceptual language similarity: comparison and evaluation](https://mlsanity.com/paper/2305.13401/) (2023)
- [XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese](https://mlsanity.com/paper/2401.06832/) (2024)
- [Towards scalable efficient on-device ASR with transfer learning](https://mlsanity.com/paper/2407.16664/) (2024)

## Abstract (arXiv)

This paper investigates how language similarity can improve cross-lingual transfer for automatic speech recognition (ASR) in extremely low-resource settings. Warlpiri, an Australian Aboriginal language, has very limited transcribed speech data, making transfer learning essential. We propose a framework combining acoustic similarity from pre-trained speech models with linguistic similarity based on typology, phoneme inventories, grammatical, and syntactic features to rank high-resource source languages and evaluate their effectiveness for ASR transfer to Warlpiri. Experiments with Whisper show that acoustically and typologically similar languages outperform monolingual and multilingual baselines. Assamese and Hindi achieve substantial reductions in word and character error rates. Correlation analysis further indicates that acoustic similarity is the strongest predictor of fine-tuning performance, while phoneme inventory and typological similarity better explain zero-shot transfer.