Multitask Learning with CTC and Segmental CRF for Speech Recognition
Table 2: Results of three types of acoustic features.
['Model', 'Features', 'Dim', 'dev', 'eval']
We only show results of using LSTMs with 250 dimensional hidden states. The interpolation weight was set to be 0.5. In our experiments, tuning the interpolation weight did not further improve the recognition accuracy. The improvement for FBANK features is much larger than fMLLR features. In particular, with multitask learning, the recognition accuracy of our CTC system with best path decoding is comparable to the results obtained by Graves et al.