Academic

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

arXiv:2602.23388v1 Announce Type: cross Abstract: The rising demand for inclusive speech technologies amplifies the need for multilingual datasets for Natural Language Processing (NLP) research. However, limited awareness of existing task-specific resources in low-resource languages hinders research. This challenge is especially acute in linguistically diverse countries, such as India. Cross-task profiling of existing Indian speech datasets can alleviate the data scarcity challenge. This involves investigating the utility of datasets across multiple downstream tasks rather than focusing on a single task. Prior surveys typically catalogue datasets for a single task, leaving comprehensive cross-task profiling as an open opportunity. Therefore, we propose Task-Lens, a cross-task survey that assesses the readiness of 50 Indian speech datasets spanning 26 languages for nine downstream speech tasks. First, we analyze which datasets contain metadata and properties suitable for specific tasks

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Swati Sharma, Divya V. Sharma, Anubha Gupta · · 1 min read · 21 views
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arXiv:2602.23388v1 Announce Type: cross Abstract: The rising demand for inclusive speech technologies amplifies the need for multilingual datasets for Natural Language Processing (NLP) research. However, limited awareness of existing task-specific resources in low-resource languages hinders research. This challenge is especially acute in linguistically diverse countries, such as India. Cross-task profiling of existing Indian speech datasets can alleviate the data scarcity challenge. This involves investigating the utility of datasets across multiple downstream tasks rather than focusing on a single task. Prior surveys typically catalogue datasets for a single task, leaving comprehensive cross-task profiling as an open opportunity. Therefore, we propose Task-Lens, a cross-task survey that assesses the readiness of 50 Indian speech datasets spanning 26 languages for nine downstream speech tasks. First, we analyze which datasets contain metadata and properties suitable for specific tasks. Next, we propose task-aligned enhancements to unlock datasets to their full downstream potential. Finally, we identify tasks and Indian languages that are critically underserved by current resources. Our findings reveal that many Indian speech datasets contain untapped metadata that can support multiple downstream tasks. By uncovering cross-task linkages and gaps, Task-Lens enables researchers to explore the broader applicability of existing datasets and to prioritize dataset creation for underserved tasks and languages.

Executive Summary

The article introduces Task-Lens, a cross-task survey that evaluates the readiness of 50 Indian speech datasets for nine downstream speech tasks. The survey reveals that many datasets contain untapped metadata that can support multiple tasks, highlighting the potential for cross-task linkages and gaps. Task-Lens enables researchers to explore the broader applicability of existing datasets and prioritize dataset creation for underserved tasks and languages, ultimately contributing to the development of inclusive speech technologies.

Key Points

  • ▸ Task-Lens is a cross-task survey that assesses the readiness of Indian speech datasets for multiple downstream tasks
  • ▸ The survey analyzes 50 datasets spanning 26 languages for nine speech tasks
  • ▸ The study identifies tasks and languages that are critically underserved by current resources

Merits

Comprehensive Analysis

Task-Lens provides a thorough evaluation of Indian speech datasets, uncovering cross-task linkages and gaps

Practical Applications

The study enables researchers to explore the broader applicability of existing datasets and prioritize dataset creation

Demerits

Limited Scope

The study focuses on Indian speech datasets, which may limit its generalizability to other languages and regions

Expert Commentary

The Task-Lens study is a significant contribution to the field of NLP, as it highlights the importance of considering cross-task linkages and gaps in the development of speech technologies. The study's findings have important implications for the development of inclusive speech technologies, particularly for low-resource languages. However, further research is needed to explore the generalizability of the study's findings to other languages and regions. Ultimately, Task-Lens has the potential to inform the development of more effective speech technologies and promote language diversity in the field of NLP.

Recommendations

  • ✓ Future studies should explore the application of Task-Lens to other languages and regions
  • ✓ Researchers should prioritize the development of datasets for underserved tasks and languages, as identified by the Task-Lens study

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