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

arXiv: https://arxiv.org/abs/2202.09529 · HTML: https://mlsanity.com/paper/2202.09529/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2022.

## Built on by (most cited first, 4 of 4)

- [No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation](https://mlsanity.com/paper/2310.06590/) (2023)
- [Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology](https://mlsanity.com/paper/2508.18288/) (2025)
- [Can Layer-wise SSL Features Improve Zero-Shot ASR Performance for Children's Speech?](https://mlsanity.com/paper/2508.21225/) (2025)
- [Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation](https://mlsanity.com/paper/2606.19797/) (2026)

## Abstract (arXiv)

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.