Speaker Adaptation for End-to-End CTC Models
Table 4: WERs (%) of unsupervised KLD regularization adaptation for the word CTC model on the 7-speaker set..
['Adapt. type', '# of utt.', '[ITALIC] α=0', '[ITALIC] α=0.2', '[ITALIC] α=0.5', '[ITALIC] α=0.8']
We obtain decoded hypotheses from the SI word CTC model on adaptation data, and use them as targets in adaptation. The hypotheses inevitably have errors, requiring better regularization to prevent overfitting towards incorrect targets. Adaptation without regularization on 10 utterances regresses. The unsupervised KLD adaptation shows modest improvements compared with supervised scenario. The best WER is 16.92%, or 1.9% relative WERR over the baseline SI word CTC model, by adapting all the layers on 200 utterances with α=0.2.