Twin Regularization for online speech recognition
Table 1: PER(%) on the TIMIT dataset obtained with various RNN architectures and input features. Our approach UniTwin shows stable improvement in all cases. BiDir corresponds to the off-line recognition with bidirectional networks (reported here to provide a lower bound for the error rates of the online models).
['[EMPTY]', 'MFCC UniDir', 'MFCC UniTwin', 'MFCC BiDir', 'FBANK UniDir', 'FBANK UniTwin', 'FBANK BiDir', 'fMLLR UniDir', 'fMLLR UniTwin', 'fMLLR BiDir']
To provide a thorough assessment of our methodology, several RNNs models and features are considered. The results with off-line bidirectional models (BiDir columns) are reported only to provide a lower bound for the error rates that can be achieved with an online model. Moreover, to ensure a more accurate comparison, five experiments varying the initialization seeds were conducted for each RNN model and input feature.