Simplified End-to-End MMI training and voting for ASR
Table 1: WER of various models on the WSJ corpus. In [13] the CTC baseline is better than ours (5.48%/9.12% with the same architecture and ext. LM), and the eval92 set is used as a validation set.
['Model', 'LM', 'WER% eval92', 'WER% dev93']
We consider two decoding LMs. The WSJ standard pruned trigram model (std.), and the extended-vocabulary pruned trigram model (ext.). We compare our end-to-end MMI (EEMMI) model to CTC under the same conditions. We see a consistent improvement in WER of EEMMI using bi-grams train LM compared to CTC. It can be observed, that attention models are inferior to phonemes-based CTC and to our method. These well established alignments enable the use of our simple voting scheme. We use models with the same NN architecture we described, trained separately end-to-end. We note that simple posteriors averaging does not work well with CTC, even using models with identical architectures. We perform decoding only once, on the averaged outputs of the models.