@inproceedings{matassoni-etal-2026-phonetic,
    title = "Phonetic-based Ranking for Improved Pseudo-Labeling in Low-Resource {ASR}",
    author = "Matassoni, Marco  and
      Gretter, Roberto  and
      Daniele, Falavigna  and
      Nawar, Mohamed Nabih Ali Mohamed  and
      Brutti, Alessio  and
      Negri, Matteo  and
      Cettolo, Mauro  and
      Gaido, Marco  and
      Papi, Sara  and
      Bentivogli, Luisa",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/test-year-match/2026.lrec-1.795/",
    doi = "10.63317/338dnb8n7e85",
    pages = "10130--10139",
    abstract = "The rise of large language models has boosted speech and language technologies; however, where transcripts of audio data are limited, the performance of current technology is not yet satisfactory. One common strategy to tackle data scarcity is leveraging pseudo-labels, for example automatically transcribing data with a pre-trained ASR. One critical issue of this approach is assessing the quality of the automatic transcriptions, that may be rather bad for low-resourced languages. While several filtering approaches exist in literature, they typically work with decent pre-trained ASR models but may fail otherwise. In this work we propose a phonetic-based ranking, enabling an effective selection with controllable computational resources; the resulting subset of pseudo-labels serves as additional material for fine-tuning the source ASR models. Experiments on common benchmarks in three low-resource languages demonstrate the effectiveness of the proposed approach, yielding up to a 3-point reduction in WER."
}