Sequence Prediction with Neural Segmental Models
Table 5.4: Phoneme error rates (%) on the development set comparing multi-stage training followed by end-to-end fine-tuning (2s+ft) and end-to-end training from random initialization (e2e).
['weight function', 'loss', '2s+ft', 'e2e']
, we conduct experiments to see whether it is possible to train segmental models end to end from random initialization. We use the FC weight function and a maximum duration of 30 frames, the same setting as in the multi-stage experiments. Each loss is optimized with vanilla SGD for 20 epochs with step size 0.1 and gradient clipping with norm 5. The best performing model, chosen in the first 20 epochs, is trained for another 20 epochs with vanilla SGD step size 0.75 decayed by 0.75 after each epoch. Dropout of rate 0.2 is used throughout the training process. No other regularizers are used except early stopping on the development set.