根据语音样本差异动态调整增强策略，提升语音识别准确率。
Sample adaptive data augmentation with progressive scheduling
- 按样本损失自适应计算增强参数，实现个性化数据增强。
- 训练中逐步提高增强概率，在测试集上最高降低8.13%错误率。
- 适合需要高鲁棒性的语音识别场景，尤其对噪声和语速变化敏感的数据。
数据增强是提升自动语音识别（ASR）模型鲁棒性的常用方法。传统做法对所有训练样本采用固定增强策略，但同一批次内样本在背景噪声、语速等方面存在差异，固定策略可能导致模型陷入次优状态。此外，模型能力随训练阶段变化，固定策略难以适配。为此，本文提出样本自适应数据增强与渐进调度方法（PS-SapAug）。该方法采用两阶段训练，通过混合归一化根据每个样本的损失动态计算其增强参数，并在训练过程中逐步增加增强概率。在Aishell-1和Librispeech-100h等主流ASR基准数据集上验证，该方法在LibriSpeech-100h test-clean上降低8.13%的单词错误率（WER），test-other降低6.23%，AISHELL-1测试集降低5.26%，显著提升了模型性能并减少了错误。
原文摘要 · Abstract (English)
Data augmentation is a widely adopted technique utilized to improve the robustness of automatic speech recognition (ASR). Employing a fixed data augmentation strategy for all training data is a common practice. However, it is important to note that there can be variations in factors such as background noise, speech rate, etc. among different samples within a single training batch. By using a fixed augmentation strategy, there is a risk that the model may reach a suboptimal state. In addition to the risks of employing a fixed augmentation strategy, the model's capabilities may differ across various training stages. To address these issues, this paper proposes the method of sample-adaptive data augmentation with progressive scheduling(PS-SapAug). The proposed method applies dynamic data augmentation in a two-stage training approach. It employs hybrid normalization to compute sample-specific augmentation parameters based on each sample's loss. Additionally, the probability of augmentation gradually increases throughout the training progression. Our method is evaluated on popular ASR benchmark datasets, including Aishell-1 and Librispeech-100h, achieving up to 8.13% WER reduction on LibriSpeech-100h test-clean, 6.23% on test-other, and 5.26% on AISHELL-1 test set, which demonstrate the efficacy of our approach enhancing performance and minimizing errors.
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