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

2026-03-02 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, extended

Summary

Task-Lens is a novel cross-task survey that profiles the readiness of 50 Indian speech datasets, encompassing 26 languages and over 91,257 hours of audio, for nine downstream speech tasks. It addresses the critical need for multilingual datasets in low-resource languages, particularly in linguistically diverse regions like India, where awareness of existing task-specific resources is limited. The methodology involves dataset discovery, filtering, feature extraction, and utility mapping to assess which datasets contain suitable metadata for specific tasks. Task-Lens identifies tasks and Indian languages that are critically underserved by current resources, such as speaker verification/identification, audio deepfake detection, and emotion recognition, which collectively account for only 9,000 to 13,000 hours of data. It also highlights languages like Bhojpuri, Dogri, and Kashmiri with less than 400 hours of speech, while revealing that many existing datasets possess untapped metadata for broader applicability.

Key takeaway

For research scientists developing speech technologies for Indian languages, Task-Lens provides a clear roadmap for efficient dataset selection and identifies critical gaps. You should use this profiling to quickly discover suitable corpora for your specific tasks, reducing time spent on data curation. Furthermore, you can identify underserved languages and tasks to guide future data collection efforts, ensuring more inclusive and robust multilingual speech model development.

Key insights

Cross-task profiling of Indian speech datasets reveals untapped utility and critical resource gaps for low-resource languages and specific tasks.

Principles

• Metadata analysis enables cross-task utility assessment.
• Dataset readiness is determined by required feature presence.
• Resource scarcity is acute in linguistically diverse regions.

Method

Task-Lens systematically profiles speech datasets through discovery, filtering, feature extraction, and utility mapping, using a task-feature relevance matrix to determine "Task-Ready" status based on required metadata.

In practice

• Identify datasets with rich metadata for multi-task reuse.
• Prioritize data collection for underserved languages and tasks.
• Enhance existing datasets with missing key metadata.

Topics

• Indian Speech Datasets
• Cross-Task Profiling
• Low-Resource NLP
• Speech Technology
• Dataset Utility

Best for: Research Scientist, AI Researcher, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.