Data–selective Transfer Learning for Multi–Domain Speech Recognition
Table 1: WER (%) of models trained on full set
['Features', 'RD', 'TV', 'CT', 'MT', 'TK', 'RS', 'Total']
These results show the large variety in performance among domains, from 17–18% for read speech and radio broadcasts to 51% for television broadcasts. The use of DNN front–ends provides a 25% relative improvement in performance against PLP features; which is consistent across domains and follows results previously seen in the literature