ASAPP-ASR: Multistream CNN and Self-Attentive SRUfor SOTA Speech Recognition
Table 3: WER (in %) comparison among different setups.
['[BOLD] Setup', '[BOLD] Dev clean', '[BOLD] Dev other', '[BOLD] Test clean', '[BOLD] Test other']
The setup of TDNN-F + 4-gram is a baseline with the TDNN-F acoustic model of the Kaldi Librispeech recipe rescored with our custom 4-gram LM. As compared to this basline, multistream CNN achieves a relative WER improvement of 14% on test-other, demonstrating its robustness. The lattice rescoring with the TDNN-LSTM language model further reduces the WER by 14% relative, showing the better modeling capability of a neural language model.