Segmental Recurrent Neural Networks for End-to-end Speech Recognition
Table 2: Results of hierarchical subsampling networks. d(w) and d(hj) denote the dimension of w and hjdj in Eqs. (3) and (7) respectively. layers denotes the number of LSTM layers and hidden is the dimension of the LSTM cells. We reduced the dimension of hjdj from the LSTM output for computational reasons. conc is short for the concatenating operation.
['System', '[ITALIC] d( [BOLD] w)', '[ITALIC] d( [BOLD] h [ITALIC] jdj)', 'layers', 'hidden', 'PER(%)']
We observed that concatenating all the hidden states in the subsampling window did not yield lower phone error rate (PER) than using the simple skipping approach, which may be due to the fact that the TIMIT dataset is small and it prefers a smaller model. On the other hand, adding the hidden states in the subsampling window together worked even worse, possibly due to that the sequential information in the subsampling window was flattened. In the following experiments, we sticked to the skipping method, and using two subsampling layers.