@article{yang-etal-2026-tg,
    title = "{TG}-{ASR}: Translation-Guided Learning with Parallel Gated Cross Attention for Low-Resource Automatic Speech Recognition",
    author = "Yang, ChengYeh  and
      Wang, Chien-Chun  and
      Chen, Li-Wei  and
      Lee, Hung-Shin  and
      Wang, Hsin-Min  and
      Chen, Berlin",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.790/",
    pages = "10071--10081",
    abstract = "Low-resource automatic speech recognition remains a critical challenge due to the scarcity of transcribed data for many languages.Taiwanese Hokkien exemplifies this problem as, although extensive speech content exists in television dramas and online videos, transcriptions are scarce and most available subtitles are in Mandarin.To address this gap, this paper presents TG-ASR for Taiwanese drama speech recognition, a translation-guided ASR framework that leverages multilingual translation embeddings to enhance recognition in low-resource conditions.The framework centers on the parallel gated cross-attention (PGCA) mechanism, which adaptively integrates embeddings from multiple auxiliary languages into the ASR decoder.This mechanism enables robust cross-linguistic semantic guidance while maintaining stable optimization and avoiding interference between languages.To support future research, we release YT-THDC, a 30-hour corpus of Taiwanese drama speech with aligned Mandarin subtitles and manually verified Taiwanese transcriptions.Extensive experiments and analysis identify which auxiliary languages most effectively improve Taiwanese ASR, achieving a 13.51{\%} relative reduction in character error rate and demonstrating the potential of translation-guided learning for underrepresented languages in real-world scenarios."
}