# Language Family Matters: Evaluating LLM-Based ASR Across Linguistic Boundaries

arXiv: https://arxiv.org/abs/2601.18899 · HTML: https://mlsanity.com/paper/2601.18899/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [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)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward](https://mlsanity.com/paper/2411.03866/) (2024)
- [AlignFormer: Modality Matching Can Achieve Better Zero-shot Instruction-Following Speech-LLM](https://mlsanity.com/paper/2412.01145/) (2024)
- [Improved acoustic word embeddings for zero-resource languages using multilingual transfer](https://mlsanity.com/paper/2006.02295/) (2020)
- [Speech LLMs in Low-Resource Scenarios: Data Volume Requirements and the Impact of Pretraining on High-Resource Languages](https://mlsanity.com/paper/2508.05149/) (2025)
- [Zero-resource Speech Translation and Recognition with LLMs](https://mlsanity.com/paper/2412.18566/) (2024)
- [Ideal-LLM: Integrating Dual Encoders and Language-Adapted LLM for Multilingual Speech-to-Text](https://mlsanity.com/paper/2409.11214/) (2024)

## Built on by (most cited first, 2 of 2)

- [MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond](https://mlsanity.com/paper/2607.22100/) (2026)
- [Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs](https://mlsanity.com/paper/2609.38106/) (2026)

## Abstract (arXiv)

Large Language Model (LLM)-powered Automatic Speech Recognition (ASR) systems achieve strong performance with limited resources by linking a frozen speech encoder to a pretrained LLM via a lightweight connector. Prior work trains a separate connector per language, overlooking linguistic relatedness. We propose an efficient and novel connector-sharing strategy based on linguistic family membership, enabling one connector per family, and empirically validate its effectiveness across two multilingual LLMs and two real-world corpora spanning curated and crowd-sourced speech. Our results show that family-based connectors reduce parameter count while improving generalization across domains, offering a practical and scalable strategy for multilingual ASR deployment.