Bidirectional neural network for pathological voice detection

We showed in our recent work that Bidirectional neural network (BNN) is a powerful tool for feature compensation in automatic speech recognition systems. In this paper, we have introduced BNN as feature compensator for better discriminating of pathological voices from normal subjects. Mel-Frequency Cepstral Coefficients (MFCCs) were extracted from each frame of sample voices and were compensated in two steps. First, BNN is trained with both normal and pathological feature vectors. Our hypothesis is that BNN can extract useful knowledge about the patterns of each class during training step. In second step, MFCC feature vectors feed into BNN and compensate according to latent knowledge of BNN. In the last step, Compensated MFCCs are classified as pathological or normal by HMMs. We achieved 4.67%, 2.81% and 2.24% improvement in measures of specificity, accuracy and sensitivity by compensated feature vectors compared to the original feature vectors. Results corroborated our hypothesis about the ability of BNN in compensation of feature vectors in a way that these features become more suitable for detection of pathological voices from normal ones.