End-to-end Training Approaches for Discriminative Segmental Models
Table 1: Frame error rates for different encoder architectures.
['[EMPTY]', 'feat', 'dev', 'test']
Since TIMIT is phonetically transcribed, we have access to phone labels for each individual frame. We first train LSTM frame classifiers with cross entropy loss at each frame. This LSTM will serve as our feature encoder later on, and training such LSTM corresponds to the first stage of two-stage learning. LSTM parameters are initialized uniformly in the range [−0.1,0.1]. We compare AdaGrad with step sizes in {0.01,0.02,0.04} and RMSProp with step size 0.001 and decay 0.9. Mini-batch size is always one utterance. Both optimizers are run for 50 epochs. We choose the best performing model according to the frame error rate on the development set, also known as early stopping. No gradient clipping is used during training. We observe that the best performing LSTM achieves a comparable frame error rate as the CNN. With dropout, the frame error rate is further lowered.