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home / papers / 2026 / Task-Lens: Cross-Task Utility Based Speech Dataset Profiling for Low-Resource Indian Languages

Task-Lens: Cross-Task Utility Based Speech Dataset Profiling for Low-Resource Indian Languages

Summary

Speech datasets for Indian languages are scattered, and researchers often do not know which ones exist or what they can be used for. Earlier surveys usually list resources for a single task, such as ASR or emotion recognition. Task-Lens instead asks how ready each existing corpus is for several downstream tasks, including tasks its creators did not intend. The authors followed PRISMA guidelines. They searched publication venues and data portals such as OpenSLR, Bhashini, AI4Bharat and Hugging Face, screened candidates in two stages and kept 50 datasets. For each dataset they recorded 10 standardized features. A task-feature relevance matrix then marks each feature as Required, Optional or Not Applicable for nine speech tasks. A corpus counts as "Task-Ready" for a task when it meets all Required features. The survey finds that many corpora contain unused metadata that could support additional tasks. It also suggests task-specific enhancements and points out tasks and languages with too few resources. This helps researchers reuse existing data and decide where new data collection is most needed.

Key points

  • Scale of the survey: 50 Indian speech datasets, 26 languages and more than 91,257 hours of audio. The final comprehensive search was run on October 16, 2025.
  • Pipeline: four stages, namely dataset discovery, filtering, feature extraction and utility mapping. Synthetic speech was included only when its provenance and generation method were documented.
  • Nine tasks covered (T1–T9):
    • monolingual and multilingual ASR
    • Language Identification
    • Speaker Verification/Identification
    • Audio Deepfake Detection
    • Speech Emotion Recognition
    • monolingual and multilingual TTS
    • Gender Recognition
  • Example datasets profiled: FLEURS, IndicSUPERB, IndicVoices-R, IndicSynth, LAHAJA, Svarah, NISP, IIITH-ILSC, EmoTa, SUBESCO and IndieFake.
  • Comparison with closest prior work: it goes beyond Shrishrimal et al. (2012), which covers nothing after 2012, and Poria and Huang (2025), which treats speech only in passing. Per-dataset feature values are released as supplementary material.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/43kj7ptiob6p

@inproceedings{sharma2026task,
  title = {{Task-Lens: Cross-Task Utility Based Speech Dataset Profiling for Low-Resource Indian Languages}},
  author = {Sharma, Swati and Sharma, Divya V. and Gupta, Anubha},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/43kj7ptiob6p},
  url = {https://doi.org/10.63317/43kj7ptiob6p}
}

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