A comparison of techniques for language model integration in encoder-decoder speech recognition
Table 2: Word error rates (%) on Google voice search (VS14K) and dictation data sets (D15K) for the baseline model and fusion approaches.
['Model', 'VS14K', 'D15K']
All of the fusion approaches improve performance over the baseline model for VS14K, but for D15K deep fusion suffers a minor degradation compared to baseline. As with Switchboard, on both of these data sets shallow fusion is again the best performer, although it is tied with cold fusion on VS14K. Finally, deep fusion has no or negligible gain over baseline, suggesting that deep fusion does not scale well with data.