  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.
