LoASR-Bench: Evaluating Large Speech Language Models on Low-Resource Automatic Speech Recognition Across Language Families

• URL: http://arxiv.org/abs/2603.20042v1
• Date: Fri, 20 Mar 2026 15:26:34 GMT
• Title: LoASR-Bench: Evaluating Large Speech Language Models on Low-Resource Automatic Speech Recognition Across Language Families
• Abstract summary: Large language models (LLMs) have driven substantial advances in speech language models (SpeechLMs)<n>LoASR-Bench comprises 25 languages from 9 language families, featuring both Latin and non-Latin scripts.<n> Experimental results highlight the limitations of the latest SpeechLMs in handling real-world low-resource languages.
• Score: 53.111005425237245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have driven substantial advances in speech language models (SpeechLMs), yielding strong performance in automatic speech recognition (ASR) under high-resource conditions. However, existing benchmarks predominantly focus on high-resource languages, leaving the ASR behavior of SpeechLMs in low-resource languages insufficiently understood. This gap is critical, as practical ASR systems must reliably support low-resource languages and generalize across diverse language families, and it directly hinders the deployment of SpeechLM-based ASR in real-world multilingual scenarios. As a result, it is essential to evaluate SpeechLMs on low-resource languages to ensure their generalizability across different language families. To address this problem, we propose \textbf{LoASR-Bench}, a comprehensive benchmark designed to evaluate \textbf{lo}w-resource \textbf{a}utomatic \textbf{s}peech \textbf{r}ecognition (\textbf{ASR}) of the latest SpeechLMs across diverse language families. LoASR-Bench comprises 25 languages from 9 language families, featuring both Latin and non-Latin scripts, enabling cross-linguistic and cross-script assessment of ASR performance of current SpeechLMs. Experimental results highlight the limitations of the latest SpeechLMs in handling real-world low-resource languages.

Related papers

• Multimodal In-context Learning for ASR of Low-resource Languages [16.078416187950207]
  In-context learning (ICL) with large language models (LLMs) addresses this problem.<n>This paper investigates whether speech LLMs can learn unseen languages with multimodal ICL (MICL)<n>Cross-lingual transfer learning improves MICL efficiency on target languages without training on them.
  arXiv  Detail & Related papers  (2026-01-09T10:52:23Z)
• Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages [76.14451035425229]
  We introduce Omnilingual ASR, a large-scale automatic speech recognition system.<n>It scales self-supervised pre-training to 7B parameters to learn robust speech representations.<n>It expands coverage to over 1,600 languages, including over 500 never before served by ASR.
  arXiv  Detail & Related papers  (2025-11-12T19:48:09Z)
• Speech LLMs in Low-Resource Scenarios: Data Volume Requirements and the Impact of Pretraining on High-Resource Languages [9.577509224534323]
  Large language models (LLMs) have demonstrated potential in handling spoken inputs for high-resource languages, reaching state-of-the-art performance in various tasks.<n>This work investigates the use of Speech LLMs for low-resource Automatic Speech Recognition using the SLAM-ASR framework.<n>We show that leveraging mono- or multilingual projectors pretrained on high-resource languages reduces the impact of data scarcity.
  arXiv  Detail & Related papers  (2025-08-07T08:33:42Z)
• Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages [1.758729398520438]
  We benchmark the performance of two fine-tuned multilingual ASR models, MMS and XLS-R, on five typologically diverse low-resource languages.<n>Our findings show that MMS is best suited when extremely small amounts of training data are available, whereas XLS-R shows parity performance once training data exceed one hour.
  arXiv  Detail & Related papers  (2025-06-20T19:59:49Z)
• Efficient Multilingual ASR Finetuning via LoRA Language Experts [59.27778147311189]
  This paper proposes an efficient finetuning framework for customized multilingual ASR via prepared LoRA language experts based on Whisper.<n>Through LoRA expert fusion or knowledge distillation, our approach achieves better recognition performance on target languages than standard fine-tuning methods.<n> Experimental results demonstrate that the proposed models yield approximately 10% and 15% relative performance gains in language-aware and language-agnostic scenarios.
  arXiv  Detail & Related papers  (2025-06-11T07:06:27Z)
• mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks [14.0147704462457]
  We introduce mSTEB, a new benchmark to evaluate the performance of large language models (LLMs) on a wide range of tasks.<n>We evaluate the performance of leading LLMs such as Gemini 2.0 Flash and GPT-4o (Audio) and state-of-the-art open models such as Qwen 2 Audio and Gemma 3 27B.
  arXiv  Detail & Related papers  (2025-06-10T03:15:08Z)
• Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling [50.62091603179394]
  Whisper, one of the most advanced ASR models, handles 99 languages effectively.<n>However, Whisper struggles with unseen languages, those not included in its pre-training.<n>We propose methods that exploit these relationships to enhance ASR performance on unseen languages.
  arXiv  Detail & Related papers  (2024-12-21T04:05:43Z)
• SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition [55.2480439325792]
  Speech Meta In-Context LEarning (SMILE) is an innovative framework that combines meta-learning with speech in-context learning (SICL)<n>We show that SMILE consistently outperforms baseline methods in training-free few-shot multilingual ASR tasks.
  arXiv  Detail & Related papers  (2024-09-16T16:04:16Z)
• Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages [60.162717568496355]
  Large language models (LLMs) have been pre-trained on multilingual corpora. Their performance still lags behind in most languages compared to a few resource-rich languages.
  arXiv  Detail & Related papers  (2024-02-19T15:07:32Z)
• Efficient Spoken Language Recognition via Multilabel Classification [53.662747523872305]
  We show that our models obtain competitive results while being orders of magnitude smaller and faster than current state-of-the-art methods. Our multilabel strategy is more robust to unseen non-target languages compared to multiclass classification.
  arXiv  Detail & Related papers  (2023-06-02T23:04:19Z)
• Language-universal phonetic encoder for low-resource speech recognition [28.21805271848413]
  We leverage International Phonetic Alphabet (IPA) based language-universal phonetic model to improve low-resource ASR performances. Our approach and adaptation are effective on extremely low-resource languages, even within domain- and language-mismatched scenarios.
  arXiv  Detail & Related papers  (2023-05-19T10:24:30Z)
• That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages [72.9927937955371]
  We use the resources existing in other languages to train a multilingual automatic speech recognition model. We observe significant improvements across all languages in the multilingual setting, and stark degradation in the crosslingual setting. Our analysis uncovered that even the phones that are unique to a single language can benefit greatly from adding training data from other languages.
  arXiv  Detail & Related papers  (2020-05-16T22:28:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.