Performance Improvements of Probabilistic Transcript-adapted ASR with Recurrent Neural Network and Language-Specific Constraints
Table 1: PERs on the evaluation and development sets of Greek using various language-specific constraints. CL=cross-lingual baseline. PT=PT adaptation. G2P, dict, WLM: defined in the text.
['System', 'PER% (dev)', 'PER% (eval)']
We observe a consistent reduction in phone error rates after incorporating the G2P constraints. Using PT adaptation with the cross-lingual baseline system significantly improves PER performance with a relative PER reduction of 9%. Adaption with PTs subjected to the G2P constraints further improves ASR performance, with the WLM constraints providing the largest reduction of 10% PER on the evaluation set.