Multitask Learning with CTC and Segmental CRF for Speech Recognition
Table 1: Phone error rates of baseline CTC and SRNN models.
['Model', 'Features', '#Layer', 'Dim', 'dev', 'eval']
The FBANK features are 120-dimensional with delta and delta-delta coefficients, and the fMLLR features are 40-dimensional, which were obtained from a Kaldi baseline system. We used a 3-layer bidirectional LSTMs for feature extraction, and we used the greedy best path decoding algorithm for both models. Our SRNN and CTC achieved comparable phone error rate (PER) for both kinds of features. However, for the CTC system, Graves et al. Apart from the implementation difference of using different code bases, Graves et al.