# Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis

arXiv: https://arxiv.org/abs/2501.06810 · HTML: https://mlsanity.com/paper/2501.06810/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 12 Jan 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation](https://mlsanity.com/paper/1803.06815/) (2018)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review](https://mlsanity.com/paper/2305.16768/) (2023)
- [Exploiting Adapters for Cross-lingual Low-resource Speech Recognition](https://mlsanity.com/paper/2105.11905/) (2021)
- [Sequence-based Multi-lingual Low Resource Speech Recognition](https://mlsanity.com/paper/1802.07420/) (2018)
- [Universal Automatic Phonetic Transcription into the International Phonetic Alphabet](https://mlsanity.com/paper/2308.03917/) (2023)
- [Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation](https://mlsanity.com/paper/2006.05474/) (2020)
- [Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching](https://mlsanity.com/paper/2311.15077/) (2023)
- [Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens](https://mlsanity.com/paper/2402.02302/) (2024)
- [Differentiable Allophone Graphs for Language-Universal Speech Recognition](https://mlsanity.com/paper/2107.11628/) (2021)
- [Phoneme Level Language Models for Sequence Based Low Resource ASR](https://mlsanity.com/paper/1902.07613/) (2019)
- [The Performance Evaluation of Attention-Based Neural ASR under Mixed Speech Input](https://mlsanity.com/paper/2108.01245/) (2021)

## Built on by (most cited first, 5 of 5)

- [Multilingual Phonological Feature Recognition with Self-Supervised Speech Models](https://mlsanity.com/paper/2605.25596/) (2026)
- [How Far Do SSL Speech Models Listen for Tone? Temporal Focus of Tone Representation under Low-resource Transfer](https://mlsanity.com/paper/2511.12285/) (2025)
- [Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster](https://mlsanity.com/paper/2603.07238/) (2026)
- [Language Orthogonalization for Zero-Shot Cross-Lingual Audio Deepfake Detection](https://mlsanity.com/paper/2609.16458/) (2026)
- [Which Languages Transfer Best to Warlpiri? A Similarity-Based Study for Low-Resource ASR](https://mlsanity.com/paper/2607.10256/) (2026)

## Abstract (arXiv)

This paper examines how linguistic similarity affects cross-lingual phonetic representation in speech processing for low-resource languages, emphasizing effective source language selection. Previous cross-lingual research has used various source languages to enhance performance for the target low-resource language without thorough consideration of selection. Our study stands out by providing an in-depth analysis of language selection, supported by a practical approach to assess phonetic proximity among multiple language families. We investigate how within-family similarity impacts performance in multilingual training, which aids in understanding language dynamics. We also evaluate the effect of using phonologically similar languages, regardless of family. For the phoneme recognition task, utilizing phonologically similar languages consistently achieves a relative improvement of 55.6% over monolingual training, even surpassing the performance of a large-scale self-supervised learning model. Multilingual training within the same language family demonstrates that higher phonological similarity enhances performance, while lower similarity results in degraded performance compared to monolingual training.