In this paper, alternating weak triphone/BPE alignment supervision is
proposed to improve end-to-end model training. Towards this end, triphone and
BPE alignments are extracted using a pre-existing hybrid ASR system. Then,
regularization effect is obtained by cross-entropy based intermediate auxiliary
losses computed on such alignments at a mid-layer representation of the encoder
for triphone alignments and at the encoder for BPE alignments. Weak supervision
is achieved through strong label smoothing with parameter of 0.5. Experimental
results on TED-LIUM 2 indicate that either triphone or BPE alignment based weak
supervision improves ASR performance over standard CTC auxiliary loss.
Moreover, their combination lowers the word error rate further. We also
investigate the alternation of the two auxiliary tasks during model training,
and additional performance gain is observed. Overall, the proposed techniques
result in over 10% relative error rate reduction over a CTC-regularized
baseline system.

通过交替使用弱三音素 / BPE 对齐辅助训练模型，建议在中间层和编码器中使用三音素和 BPE 对齐计算交叉熵中间辅助损失来提高自动语音识别性能，同时降低字错率 10% 以上。