Sequence Prediction with Neural Segmental Models
Table 7.6: Letter error rates (%) for signer-independent models averaged over eight 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-independent setting, we train on three signers and use the last signer for development and testing. Specifically, the data of the last signer is split into ten folds, and we use two folds for development and the rest of the eight folds for testing. Instead of dividing the accumulated error counts by the accumulated label sequence lengths, letter error rates are averaged over the folds. We compare LSTMs trained with CTC and segmental models with FCB weight function. The loss function and training procedure stay the same except that we only run 40 epochs of vanilla SGD for segmental models. In this setting, CTC and segmental models trained end to end perform significantly better than Tandem HMMs and segmental models trained with the FC weight function in two stages.