Sequence Prediction with Neural Segmental Models
Table 7.5: Letter error rates (%) for signer-dependent models averaged over ten folds.
['[EMPTY]', 'Andy dev', 'Andy test', 'Drucie dev', 'Drucie test', 'Rita dev', 'Rita test', 'Robin dev', 'Robin test', 'Avg dev', 'Avg test']
For the signer-dependent setting, the data for each signer is split into ten folds, in which eight are for training, one is for development, and one is for testing. We report the letter error rate averaged over the 10 experiments. We train LSTMs with CTC and segmental models with the FCB weight function. Both models are trained with marginal log loss from random initialization. The encoder is a one-layer 128-unit bidirectional LSTM. For CTC, we run vanilla SGD with step sizes in {0.1,0.05,0.025} and gradient clipping with norm 5 for 200 epochs. Dropout is used with a rate of 0.2, and no other explicit regularizer is used except early stopping. Step sizes and early stopping are tuned on the development set for each individual experiment. For segmental models, we run vanilla SGD with step size 0.1 for 75 epochs. The maximum segment length is 75 frames. Other hyperparameters, such as dropout and gradient clipping, are the same as for CTC. The frame-based LSTMs trained with CTC are better than Tandem HMMs, but are behind the segmental models.