In this paper, we aim to create weak alignment supervision from an existing hybrid system to aid the end-to-end modeling of automatic speech recognition. Towards this end, we use the existing hybrid ASR system to produce triphone alignments of the training audios. We then create a cross-entropy loss at a certain layer of the encoder using the derived alignments. In contrast to the general one-hot cross-entropy losses, here we use a cross-entropy loss with a label smoothing parameter to regularize the supervision. As a comparison, we also conduct the experiments with one-hot cross-entropy losses and CTC losses with loss weighting. The results show that placing the weak alignment supervision with the label smoothing parameter of 0.5 at the third encoder layer outperforms the other two approaches and leads to about 5\% relative WER reduction on the TED-LIUM 2 dataset over the baseline. We see similar improvements when applying the method out-of-the-box on a Tagalog end-to-end ASR system.

翻译：本文旨在利用现有混合系统生成的弱对齐监督来辅助端到端自动语音识别建模。为此，我们采用现有混合ASR系统生成训练音频的三音子对齐，并在编码器特定层基于该对齐构造交叉熵损失。与标准的一热交叉熵损失不同，我们引入具有标签平滑参数的交叉熵损失来规整监督信号。作为对比，我们还分别采用一热交叉熵损失和加权CTC损失进行实验。结果表明：在编码器第三层使用标签平滑参数为0.5的弱对齐监督，其性能优于其他两种方法，在TED-LIUM 2数据集上相对基线词错误率降低约5%。将该方法直接应用于他加禄语端到端ASR系统时，同样获得了类似改进效果。