End-to-end Training Approaches for Discriminative Segmental Models
Table 4: Phone error rates for segmental models trained end to end initialized from the two-stage system trained with hinge loss.
['[EMPTY]', 'dropout', 'dev', 'test']
After two-stage training, we fine tune the encoder and segmental model jointly to further lower the training loss. The four losses are compared with and without dropout. When dropout is used, dropout rate 50% is chosen to match the rate during frame classifier training. The input layers and the output layer are scaled by 0.5 when no dropout is used. First, we initialize the models with the one trained with hinge loss above. We run AdaGrad with step size 0.001 for 10 epochs with early stopping. We observe healthy reductions in phone error rates by fine tuning the two-stage system across all loss functions. We also find that fine-tuning without dropout tends to be better than with dropout. Though fine-tuning with hinge loss leads to the most error reduction, we note that the two-stage system is trained with hinge loss. At least we are centain that the two-stage system trained with hinge loss is a descent initialization for other losses.