Improving the robustness of noisy MFCC features using minimal recurrent neural networks

We describe a novel technique for improving speech recognition performance in real environments. We investigate the special case of speech recognition in the car environment for SNRs ranging from -10 to 20 dB. Our approach makes use of a feature set that is composed of uncorrelated variables in order to create a group of neural networks each one dedicated to a sole variable of the feature vector. This technique results in neural networks of much smaller total number of weights than reported cases and consequently in faster training and execution performance. Furthermore, contextual information regarding a feature´s history is incorporated into the network by making use of recurrent neural networks. We evaluate the performance in comparison with the standard MLPs and TDNNs in order to prove that they compare favourably to them in terms of recognition improvement over a wide range of SNRs