Lattice-Based Unsupervised Test-Time Adaptationof Neural Network Acoustic Models
Table 6: WER for adaptation of the TED-LIUM model without i-vectors and the Somali model using best path as a supervision with varying fractions of the adaptation data.
['[EMPTY]', 'TED-LIUM dev', 'TED-LIUM test', 'Somali NB', 'Somali WB']
This filtering can be done by using a hard threshold, or by using only the fraction of utterances with the highest confidences. Either way one extra hyper-parameter is introduced. We experiment with the TED-LIUM model without i-vectors, and the Somali model. As can be seen from the table, filtering utterances improves results when using best path supervision. The biggest improvement can be achieved when using only 50% of the adaptation data. Even then the TED-LIUM model does not obtain similar performance as when adapted using lattices for supervision. Furthermore, adaptation of the Somali model using best path supervision only barely matches the unadapted baseline. This is probably due to the fact that the WER of the initial Somali model is high and that the lattice provides much more information than a combination of best path supervision and corresponding confidences. We also performed the same filtering experiment with lattices as supervision. We found that using a threshold of 75% – 100% achieves the best results. Overall, adaptation using lattice supervision does not benefit from filtering utterances as much as adaptation using best path supervision.