再议语音转文本生成中的插值增强方法
Revisiting Interpolation Augmentation for Speech-to-Text Generation
AI总结：
针对低资源场景下语音转文本生成系统标注数据不足的问题，本文深入研究插值增强技术的效用，发现采用合适策略可在多种任务、架构和数据规模下显著提升性能，为资源受限场景下的鲁棒S2T系统提供新方向。
摘要：
语音转文本（S2T）生成系统在低资源场景下常面临挑战，主要原因是缺乏大规模标注数据集。一种新兴解决方案是通过对输入和标签进行插值来构建虚拟训练样本，该方法在其他领域已显著提升了系统的泛化能力。尽管具备潜力，该技术在S2T任务中的应用仍未得到充分探索。本文围绕几个关键问题，深入研究了插值增强的效用。研究结果表明，采用合适的插值增强策略可在不同任务、架构和数据规模下显著提升性能，为资源受限场景下构建更鲁棒的S2T系统提供了可行方向。
英文摘要：
Speech-to-text (S2T) generation systems frequently face challenges in low-resource scenarios, primarily due to the lack of extensive labeled datasets. One emerging solution is constructing virtual training samples by interpolating inputs and labels, which has notably enhanced system generalization in other domains. Despite its potential, this technique's application in S2T tasks has remained under-explored. In this paper, we delve into the utility of interpolation augmentation, guided by several pivotal questions. Our findings reveal that employing an appropriate strategy in interpolation augmentation significantly enhances performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
