Speaker Adaptation for End-to-End CTC Models
Table 1: WERs (%) on the 7-speaker set for supervised KLD regularization adaptation for the letter CTC model.
['Adapt. type', '# of utt.', '[ITALIC] α=0', '[ITALIC] α=0.2', '[ITALIC] α=0.5', '[ITALIC] α=0.8']
For the letter CTC model, we simply apply the KLD adaptation as it has only 30 output targets and thus does not have the adaptation target sparsity issue. All three updating setups obtain minor improvements using the smallest adaptation set with 10 utterances per speaker. For adapting with 50 and 200 utterances scenarios, both adapting all layers and all hidden layers improve the baseline letter CTC model and perform better than adapting only the top softmax layer. Among the three setups, adapting only hidden layers gives the best WER 24.89% and relative word error rate reduction (WERR) 12.5% with 200 adaptation utterances and α=0.0. We consistently observe the least improvements from adapting only top layer in the following experiments, thus we do not report any results with such setup to save space.