Sequence Prediction with Neural Segmental Models
Table 5.6: Average number of minutes for one epoch of end-to-end training on TIMIT.
['weight function', 'loss', 'regular LSTM', 'pyramid LSTM']
We compare the time to train segmental models end to end. Times are measured on a 3GHz 4-core CPU. Multithreading is only used for matrix operations; all FST algorithms are implemented single-threaded. Reducing the time resolution with pyramid LSTMs significantly improves the runtime for all losses. The MLP weight function is about 2.5 times slower than the FC weight function.