In this paper, we aim to create weak alignment supervision to aid the
end-to-end modeling. 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 with or
without loss weighting, 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.

本文旨在创建弱对齐监督以辅助端到端建模，在已有的混合 ASR 系统中产生训练音频的三音素对齐。我们在编码器的某一层上使用这些对齐创建交叉熵损失。与一般的一位有效交叉熵损失或带有损失加权的方法不同，我们在此使用带有标签平滑参数的交叉熵损失来规范监督。与使用一位有效交叉熵损失和带有损失加权的 CTC 损失进行实验相比，结果显示将弱对齐监督与标签平滑参数 0.5 放置在第三个编码器层上的方法优于另外两种方法，在 TED-LIUM 2 数据集上相对词错误率减小约 5%。将该方法直接应用于一个塔加路语端到端 ASR 系统时也得到类似的改进。