Data augmentation for deep convolutional neural network acoustic modeling

Xiaodong Cui, Vaibhava Goel, Brian Kingsbury

Abstract: This paper investigates data augmentation based on label-preserving transformations for deep convolutional neural network (CNN) acoustic modeling to deal with limited training data. We show how stochastic feature mapping (SFM) can be carried out when training CNN models with log-Mel features as input and compare it with vocal tract length perturbation (VTLP). Furthermore, a two-stage data augmentation scheme with a stacked architecture is proposed to combine VTLP and SFM as complementary approaches. Improved performance has been observed in experiments conducted on the limited language pack (LLP) of Haitian Creole in the IARPA Babel program.