Towards using context-dependent symbols in CTCwithout state-tying decision trees
Table 1: WER (%) of character-based models on WSJ, with external LMs for locally normalized CTC, globally normalized CTC-G (e.q. (2)), CTC-GB with CD blanks. CDE denotes networks with the deep CD-symbol embedder. With the exception of tri-char models, we average 3 independent training runs.
['Model', 'Bg LM Dev93', 'Bg LM Eval92', 'Tg LM\xa0 Dev93', 'Tg LM\xa0 Eval92']
We first confirm that locally normalized CTC with CD symbols conflicts with external language models. For a given symbol, locally normalized CTC sharply differentiates between its contexts. This has to be mitigated during decoding with low acoustic model weights, which is equivalent to increasing the temperature of its SoftMax output classifier. Globally normalized CTC-G reaches a lower overall WER with higher acoustic weights, indicating that it produces more ambiguous hypotheses, which are resolved by the LM. We observed a similar trend for other locally and globally normalized models. the optimal acoustic weight was close to 0.4 for locally normalized ones and to 1.0 for globally normalized ones. Early stopping checkpoints and acoustic weights for Eval92 have been selected by best WER on Dev93. We average the reported scores over 3 independent runs. Additionally, CDE denotes cases for which CD symbol prototypes were computed using the auxiliary embedding network instead of a look-up table of prototypes. For reference we provide LF-MMI results, however not directly comparable due to their data augmentation techniques.