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

LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects