Serialized Output Training for End-to-End Overlapped Speech Recognition
Table 3: WER (%) for different numbers of parameters and architectures for SOT model trained with the mixture of 1, 2, and 3 speakers. Test data were generated by mixing “test_clean.”
['Model Dim', 'SAA (Sec  4.2 )', '# of Params', '# of Speakers in Test Data 1', '# of Speakers in Test Data 2', '# of Speakers in Test Data 3', '# of Speakers in Test Data  [BOLD] Total']
We found that the large model size was essential for SOT to achieve good results. Note that the SOT model size of with SAA is smaller than the naive SOT because we removed one 1024-dim bidirectional LSTM layer from the encoder instead of simply inserting one 1024-dim unidirectional LSTM after the attention module.