Serialized Output Training for End-to-End Overlapped Speech Recognition
Table 1: WER(%) of 512-dim models for 2-speaker-mixed speech. Note that the WERs for single speaker speech by the single-speaker ASR were 5.4% and 5.7% for dev_clean and test_clean, respectively.
['Model (all 512-dim)', 'WER (%) dev_clean', 'WER (%) test_clean']
Surprisingly, the “first-in, first-out” training achieved a better WER than the “minimum loss speaker order” training although the former approach can be executed with a computational cost of O(1) with respect to the number of speakers. Based on this finding, we used the “first-in, first-out” training for the rest of the experiments.