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

1. Problem Statement

The development of inclusive speech technologies for Natural Language Processing (NLP) is hindered by a severe scarcity of multilingual datasets, particularly for low-resource languages in linguistically diverse regions like India.

• Data Scarcity & Awareness Gap: While many speech datasets exist, researchers often lack awareness of their full potential. Most existing surveys catalog datasets based on their original intended task (e.g., an ASR dataset is only listed for ASR), failing to recognize that rich metadata might make them suitable for other downstream tasks.
• Inefficiency: This lack of cross-task profiling leads to redundant data collection efforts and the underutilization of existing resources.
• The Challenge: There is no systematic framework to assess whether existing Indian speech datasets can be repurposed for tasks beyond their original design, nor is there a clear map of which languages and tasks are critically underserved.

2. Methodology: Task-Lens Framework

The authors propose Task-Lens, a systematic, cross-task utility profiling framework designed to evaluate the "readiness" of Indian speech datasets for multiple downstream tasks. The methodology follows the PRISMA guidelines for systematic reviews and consists of four stages:

A. Dataset Discovery & Filtering

• Sources: The authors searched peer-reviewed venues (IEEE, ACL, Interspeech, etc.) and repositories (OpenSLR, Bhashini, Hugging Face, GitHub, etc.).
• Criteria: They applied strict inclusion criteria:
  • Must contain Indian languages.
  • Must have public documentation with extractable features.
  • Must have identifiable splits or benchmark usage.
  • Synthetic speech was included only if provenance was documented.
• Final Corpus: 50 Indian speech datasets covering 26 languages and totaling 91,257 hours of audio.

B. Feature Extraction

The authors extracted 10 descriptive features ($f_1$ to $f_{10}$) from each dataset to determine utility. Key features include:

• Audio Quality: Audio presence and sampling rate ($f_1$).
• Metadata: Transcript presence ($f_2$), Language ID ($f_4$), Speaker ID ($f_6$), Emotional labels ($f_8$), Gender labels ($f_9$).
• Content: Multilingual speakers ($f_5$), Synthetic speech ($f_7$), Speech style ($f_{10}$).
• Licensing: License openness ($f_3$).

C. Task-Feature Mapping

The authors defined 9 downstream speech tasks ($T_1$ to $T_9$) and created a Task-Feature Relevance Matrix.

• Tasks:
  1. Monolingual ASR ($T_1$)
  2. Multilingual ASR ($T_2$)
  3. Language Identification ($T_3$)
  4. Speaker Verification/ID ($T_4$)
  5. Audio Deepfake Detection ($T_5$)
  6. Speech Emotion Recognition ($T_6$)
  7. Monolingual TTS ($T_7$)
  8. Multilingual TTS ($T_8$)
  9. Gender Recognition ($T_9$)
• Logic: For each task, specific features are marked as Required (✓) or Optional (?). A dataset is deemed "Task-Ready" for a specific task only if it possesses all required features.

D. Utility Mapping

The framework maps the extracted features of each dataset against the requirements of the 9 tasks to generate a readiness profile, identifying gaps where metadata is missing.

3. Key Contributions

1. Task-Lens Framework: A novel, cross-task survey methodology that moves beyond simple cataloging to assess dataset readiness for multiple tasks using available metadata.
2. Comprehensive Profiling: Analysis of 50 datasets across 26 languages and 9 tasks, covering over 91,000 hours of audio.
3. Identification of Untapped Utility: Demonstrated that many datasets originally designed for single tasks (e.g., ASR) contain sufficient metadata (like transcripts or speaker IDs) to support other tasks (e.g., Speaker Verification or TTS) if properly utilized.
4. Gap Analysis: Provided a granular view of resource distribution, highlighting specific languages and tasks that are critically underserved.

4. Key Results & Findings

A. Cross-Task Utility

• High Potential: Datasets such as BhasaAnuvaad, IndicVoices-R, and MS Indic Speech were found to be "Task-Ready" for up to 7 out of 9 tasks.
• Missing Links: The primary barriers preventing datasets from supporting tasks like Speaker Verification ($T_4$), Deepfake Detection ($T_5$), and Emotion Recognition ($T_6$) were the lack of Speaker IDs, Synthetic Speech labels, and Emotional Labels, respectively.
• Recommendation: Adding specific metadata to existing high-quality datasets can significantly unlock their cross-task potential without new data collection.

B. Task-Wise Data Distribution

• Well-Resourced: Language Identification ($T_3$) and Gender Recognition ($T_9$) have the highest coverage (~90,000 hours), largely due to the nature of multilingual pooling.
• Moderately Resourced: ASR ($T_1, T_2$) and TTS ($T_7, T_8$) have reasonable coverage (~60,000 hours).
• Critically Underserved:
  • Speaker Verification ($T_4$): Only ~9,800 hours.
  • Deepfake Detection ($T_5$): Only ~13,300 hours.
  • Emotion Recognition ($T_6$): Only 785 hours. This is the most severe gap, indicating a near-total lack of emotional speech data for Indian languages.

C. Linguistic Distribution

• Imbalance: Data is heavily concentrated in a few languages (e.g., Hindi, Bengali, Tamil, Telugu).
• Under-Resourced Languages: Languages like Bhojpuri, Dogri, Kashmiri, Konkani, Maithili, Manipuri, Santali, and Sindhi have less than 400 hours of data across all tasks.
• Task-Specific Gaps: For many low-resource languages, data exists for ASR but is completely absent for Speaker Verification or Emotion Recognition.

5. Significance and Impact

• Resource Optimization: Task-Lens enables researchers to repurpose existing datasets, saving time and computational resources by avoiding redundant data collection.
• Strategic Roadmap: The paper provides a clear "navigable map" for the community, identifying exactly which languages and tasks need new data collection efforts.
• Inclusivity: By highlighting the gaps in low-resource languages and critical tasks (like deepfake detection and emotion recognition), the work advocates for more equitable development of speech technologies in India.
• Future Research: The authors plan to release the full feature values and expand the framework to include code-switching and other emerging tasks, fostering a more data-centric approach to NLP in low-resource settings.

In conclusion, Task-Lens transforms scattered, single-task datasets into a structured, multi-task resource map, revealing that the "scarcity" of data is often a problem of metadata awareness rather than a total lack of audio data.