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The E2E − T Transformer and LM rescoring setups were compared (WER % and RT factor). Transcription (TR) and background (BG) texts were used to train the language models.
| Methods | Beam 20 | Beam 5 | Beam 2 | |||
|---|---|---|---|---|---|---|
| WER | RT | WER | RT | WER | RT | |
| E2E − T + LSTM − LM (BG + TR) | 15.4 | 4.46 | 16.9 | 1.89 | 16.6 | 1.87 |
| E2E − T + T − LM (BG + TR) | 14.7 | 6.37 | 15.7 | 2.62 | 15.9 | 1.96 |
| E2E − T + T − LM (TR) | 14.6 | 6.36 | 15.4 | 2.53 | 15.5 | 1.71 |
| E2E − T | 14.3 | 4.31 | 15.2 | 1.97 | 15.3 | 1.48 |
