Feature Learning in Deep Neural Networks – Studies on Speech Recognition Tasks
Table 2: WER (%) on wideband (16k) and narrowband (8k) test sets with and without narrowband training data.
['training data', '16\xa0kHz VS-T', '8\xa0kHz VS-T']
From this table, it is clear that if all training data are wideband, the DNN performs well on the wideband test set (27.5% WER) but very poorly on the narrowband test set (53.5% WER). However, if we convert VS-2 to narrowband speech and train the same DNN using mixed-bandwidth data (second row), the DNN performs very well on both wideband and narrowband speech.