Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard-300
Table 5: Detailed results with the best performing systems.
['SWB', 'ext. LM', 'xutt.', 'hub5’00 swb', 'hub5’00 chm', 'hub5’01 swb', 'hub5’01 swb2', 'hub5’01 swb2', 'rt03 swb', 'rt03 fsh']
As a contrast to our best results on Switchboard-300, we also train a seq2seq model on the 2000-hour Switchboard+Fisher data. This model consists of 10 encoder layers, and is trained for only 50 epochs. The results in Fig. For comparison with others, the 2000-hour system reaches 8.7% and 7.4% WER on rt02 and rt04. We observe that the regularization techniques, which are extremely important on the 300h setup, are still beneficial but have a significantly smaller effect.