@inproceedings{thayasivam-etal-2025-sita,
    title = "{S}i{T}a - {S}inhala and {T}amil Speaker Diarization Dataset in the Wild",
    author = "Thayasivam, Uthayasanker  and
      Gnanenthiram, Thulasithan  and
      Jeewantha, Shamila  and
      Jayawickrama, Upeksha",
    editor = "Sarveswaran, Kengatharaiyer  and
      Vaidya, Ashwini  and
      Krishna Bal, Bal  and
      Shams, Sana  and
      Thapa, Surendrabikram",
    booktitle = "Proceedings of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2025)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2025.chipsal-1.8/",
    pages = "83--92",
    abstract = "The dynamic field of speaker diarization continues to present significant challenges, despite notable advancements in recent years and the rising focus on complex acoustic scenarios emphasizes the importance of sustained research efforts in this area. While speech resources for speaker diarization are expanding rapidly, aided by semi-automated techniques, many existing datasets remain outdated and lack authentic real-world conversational data. This challenge is particularly acute for low-resource South Asian languages, due to limited public media data and reduced research efforts. Sinhala and Tamil are two such languages with limited speaker diarization datasets. To address this gap, we introduce a new speaker diarization dataset for these languages and evaluate multiple existing models to assess their performance. This work provides essential resources, a novel dataset and valuable insights from model benchmarks to advance speaker diarization for low-resource languages, particularly Sinhala and Tamil."
}