Serialized Output Training for End-to-End Overlapped Speech Recognition
Table 2: WER (%) of 512-dim models for various mixtures of training and test data. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis.
['Model (all 512-dim)', '# of Speakers in Training Data', '# of Speakers in Test Data 1', '# of Speakers in Test Data 2', '# of Speakers in Test Data 3']
We then evaluated the same 512-dim model with a training set consisting of various numbers of speakers. It should be emphasized that the SOT model did not show any degradation for the single-speaker case, sometimes even outperformed the single-speaker ASR model. This might be because of the data augmentation effect resulting from mixing training utterances.