论文标题
LPC增强：基于LPC的ASR数据增强算法，用于低资源和零资源的儿童方言
LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects
论文作者
论文摘要
本文提出了一种针对儿童低和零资源方言ASR的新型线性预测编码数据八次化方法。数据增强程序包括在LPC分析和重建过程中扰动LPC光谱的共振峰。该方法对两个新颖的儿童演讲数据集进行了评估，其中一个包含来自南加州南部的加利福尼亚英语，另一种包含来自佐治亚州亚特兰大地区的南美英语和非裔美国人英语的混合物。我们在训练HMM-DNN系统和端到端系统中测试了所提出的方法，以显示模型的能力，并证明该算法可以改善ASR性能，尤其是对于零资源方言儿童的任务，与常见的数据增强方法（例如VTLP，Speed，Speed扰动和规范）相比，算法尤其是对于零资源方言。
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.
