[论文解读] Task-Lens: Cross-Task Utility Based Speech Dataset Profiling for Low-Resource Indian Languages

Swati Sharma, Divya Sharma+1 more|arXiv (Cornell University)|Feb 16, 2026

ICT in Developing Communities被引用 0

一句话总结

Task-Lens 系统性地对 50 个印度语音数据集在 26 种语言下的九个下游任务进行跨任务剖析，以揭示跨任务就绪性、差距和语言覆盖情况，促成有针对性的数据重用与数据集创建。

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.

研究动机与目标

• 使用元数据和属性评估印度语音数据集的跨任务就绪性。
• 识别哪些数据集支持超出原始用途的多下游任务。
• 提出与任务对齐的改进以提升数据集的更广泛实用性。
• 突出服务不足的语言和任务，以指导有针对性的数据收集。

提出的方法

• 从与印度语音资源相关的同行评审刊物和注册门户进行数据集发现。
• 两阶段筛选以确保包含印地语等印度语言内容并提取可用元数据。
• 使用标准化模式提取每个数据集的 10 个描述性特征。
• 任务–特征相关性矩阵将数据集特征映射到九个下游任务。
• 定义 Task-Ready 状态：数据集满足某一任务的所有“必需”特征。

Figure 1: Task-Lens: It involves dataset discovery, dataset filtering, feature extraction, followed by utility mapping that aligns dataset features with task needs via a Task-feature relevance matrix labeled as Required and Optional or Not Applicable. A dataset is ‘Task-Ready’ for a task if it satis

实验结果

研究问题

• RQ1 每个数据集目前支持哪些任务？
• RQ2 哪些改进能够使数据集适用于跨任务应用？
• RQ3 在印度情境下，语音研究的哪些领域缺乏足够的数据集支持？
• RQ4 哪些印度语言在各任务上覆盖充足，哪些存在差距？

主要发现

• 分析覆盖了 50 个印度语音数据集，涵盖 26 种语言，总计 91,257 小时音频。
• 若干数据集（如 D4、D6、D15、D16、D18、D22、D29、D34、D35）具备支撑九个任务中七个所需的特征。
• 说话人标识、合成语音与情感标签常缺失，限制了 SV/SID、ADD 与 SER 的跨任务就绪性。
• 任务 T3（LID）和 T9（GRE）因为多语言池化与共享数据集，覆盖率较高（约 90,000 小时）。
• SER 仍是数据最不足的任务（约 785 小时），显示印度语言的关键数据空缺。

Figure 2: Distribution of total dataset duration for each task in hours for direct comparison. There is an urgent need of datasets for tasks $T_{4}$ (SV/SID), $T_{5}$ (ADD), and $T_{6}$ (SER).