  This paper proposes a novel linear prediction coding-based data aug-mentation
method for children's low and zero resource dialect ASR. The data augmentation
procedure consists of perturbing the formant peaks of the LPC spectrum during
LPC analysis and reconstruction. The method is evaluated on two novel
children's speech datasets with one containing California English from the
Southern CaliforniaArea and the other containing a mix of Southern American
English and African American English from the Atlanta, Georgia area. We test
the proposed method in training both an HMM-DNN system and an end-to-end system
to show model-robustness and demonstrate that the algorithm improves ASR
performance, especially for zero resource dialect children's task, as compared
to common data augmentation methods such as VTLP, Speed Perturbation, and
SpecAugment.
