CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition
Table 2: Ablation study on Librispeech, where the full model is our base CIF-based model (without LM) in Table.1, WER (%)
['[EMPTY]', '[BOLD] test-clean', '[BOLD] test-other']
To further verify this phenomenon, we compare the models with/without auto-regression on the Mandarin dataset of AISHELL-2 but find they achieve comparable performance. Since the acoustic boundaries between Mandarin characters are much more clear, we suspect the importance of auto-regression is related to the clearness of acoustic boundary between output labels. Besides, the proposed support strategies (scaling strategy, quantity loss and tail handling) for the CIF-based model all provide clear improvements. Among them, the quantity loss is the most important since ablating it causes the largest performance loss and brings large learning instability. The introduced CTC loss also benefits to the CIF-based model but not as important as others.