Sequence Prediction with Neural Segmental Models
Table 7.3: Average number of minutes for one epoch of end-to-end training on TIMIT.
['CTC', '[EMPTY]', 'regular LSTM 104', 'pyramid LSTM 44']
Pyramid LSTMs trained with CTC are the best performer while also being the most efficient to train. Due to smaller and simpler search spaces, frame-based LSTMs trained with CTC decode faster than segmental models. While frame-based LSTMs train and decode faster, segmental models are more flexible, so they might be improved with additional feature functions. Explicitly hypothesizing segments can serve as a prior, so segmental models might perform better in low-resource settings.