CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition
Table 1: Comparison with other end-to-end models on Librispeech, word error rate (WER) (%)
['[BOLD] Model', '[BOLD] test-clean  [BOLD] w/o LM', '[BOLD] test-clean  [BOLD] w/ LM', '[BOLD] test-other  [BOLD] w/o LM', '[BOLD] test-other  [BOLD] w/ LM']
On the Librispeech dataset, we use the word pieces as the output labels. Since the word pieces are obtained without referring to any acoustic knowledge, the acoustic boundary between adjacent labels may be blurred. Even so, our big CIF-based model still achieves a word error rate (WER) of 2.86% on test-clean and 8.08% on Besides, the above CIF-based models all apply a very low encoded frame rate (12.5 Hz) for reducing the computational burden. Switching to a higher frame rate may further improve their performance.