@inproceedings{riyadh-etal-2026-speechlm,
    title = "{S}peech{LM} for Automatic Speech Recognition in Low-resource Languages",
    author = "Riyadh, Md Abdur Razzaq  and
      Agirre, Eneko  and
      Navas, Eva  and
      Borg, Claudia",
    editor = "Hosseini-Kivanani, Nina  and
      Brutti, Alessio  and
      Matassoni, Marco  and
      Dowerah, Sandipana  and
      Liga, Davide  and
      Schommer, Christoph",
    booktitle = "Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis ({SPEAKABLE}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.speakable-1.14/",
    doi = "10.63317/2634e2pv97js",
    pages = "125--131",
    abstract = "Multi-modal Speech Language Models (SpeechLMs) are a recent advancement in natural language processing. These SpeechLMs are instruction-tuned and optimized for general tasks. Their usefulness for Automatic Speech Recognition (ASR), particularly in relatively low-resource scenarios, remains largely understudied. This work developed SpeechLM for ASR in Basque and Maltese and studied the impact of language-adapted Large Language Model (LLM) and speech encoder within the SpeechLM for ASR. Using supervised learning, we fine-tuned LLaMA-Omni, a SpeechLM, for ASR. We have conducted comprehensive hyperparameter tuning and experimented with language-adapted SpeechLM components to improve performance and evaluated our best models on in-distribution datasets for both languages and an out-of-distribution dataset for Basque. LLaMA-Omni achieved 8.09{\%} WER in Basque and 25.65{\%} WER for Maltese on average across multiple test splits. The in-distribution results show that SpeechLM outperforms a fine-tuned ASR system under specific constraints, whereas it underperforms the baseline model on out-of-distribution Basque, indicating weaker overall robustness. We also find that a language-adapted LLM within SpeechLM improves in out-of-distribution settings when compared to the off-the-shelf LLM within SpeechLM."
}