The rising demand for inclusive speech technologies highlights the need for multilingual datasets for Natural Language Processing (NLP) research. In linguistically diverse countries such as India, limited awareness of existing task-specific resources in low-resource languages presents a significant challenge.

Task-Lens: a cross-task approach

To address this issue, researchers have developed Task-Lens, a cross-task survey of 50 Indian speech datasets spanning 26 languages. The goal is to assess the readiness of these datasets for nine speech processing tasks. The survey focuses on the utility of datasets across multiple downstream tasks, rather than on a single task, filling a gap in previous analyses.

Methodology and findings

Task-Lens analyzes which datasets contain metadata and properties suitable for specific tasks. It also proposes task-aligned enhancements to unlock the full downstream potential of the datasets. Finally, it identifies tasks and Indian languages that are significantly underserved by current resources. The findings reveal that many Indian speech datasets contain untapped metadata that can support multiple downstream tasks, enabling researchers to explore the broader applicability of existing datasets and to prioritize dataset creation for underserved tasks and languages.