Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription
Table 4: %WER of phonetic and graphemic TDNN-LSTM models with and without model smoothing on dev17b.
['Model', '%WER Ph', '%WER Gr', 'Ph/Gr Comb %WER', 'Ph/Gr Comb %Rel']
Additional systems can also be generated using simpler approaches. For example, the use of model smoothing does not require another model to be trained. Additionally by performing model smoothing the difference between the phonetic and graphemic systems is reduced (+6.0%). Though the gains from combining phonetic and graphemic systems decrease after model smoothing, dropping from 7.3 to 5.6% relative, there is still a large gain in performance in the combined systems after model smoothing, yielding a better performance than modifying the training criterion.