Twin Regularization for online speech recognition
Table 4: WER(%) for CHiME (ET-Real) and Librispeech (Test-Clean) using Li-GRU models and MFCC feats.
['[EMPTY]', 'UniDir', 'UniTwin', 'BiDir']
To further test its robustness in noisy environments, we also performed some experiments with the CHiME dataset (see first row of Tab. Moreover, to provide evidence on a larger vocabulary task, the second row of Tab. Our experiments target the online ASR scenario, therefore the results reported in the table do not consider complex techniques as multi-microphone processing, data-augmentation. Neither system combination nor lattice rescoring are used here. It is however worth noting that the effectiveness of the proposed approach is one more time confirmed.