Feature extraction using pre-trained convolutive bottleneck nets for dysarthric speech recognition

In this paper, we investigate the recognition of speech uttered by a person with an articulation disorder resulting from athetoid cerebral palsy based on a robust feature extraction method using pre-trained convolutive bottleneck networks (CBN). Generally speaking, the amount of speech data obtained from a person with an articulation disorder is limited because their burden is large due to strain on the speech muscles. Therefore, a trained CBN tends toward overfitting for a small corpus of training data. In our previous work, the experimental results showed speech recognition using features extracted from CBNs outperformed conventional features. However, the recognition accuracy strongly depends on the initial values of the convolution kernels. To prevent overfitting in the networks, we introduce in this paper a pre-training technique using a convolutional restricted Boltzmann machine (CRBM). Through word-recognition experiments, we confirmed its superiority in comparison to convolutional networks without pre-training.