Dysarthric Speech Recognition Using Variational Mode Decomposition and Convolutional Neural Networks

Dysarthric speech recognition requires a learning technique that is able to capture dysarthric speech specific features. Dysarthric speech is considered as speech with source distortion or noisy speech. Hence, as a first step speech enhancement is performed using variational mode decomposition (VMD) and wavelet thresholding. The reconstructed signals are then fed as input to convolutional neural networks. These networks learn dysarthric speech specific features and generate a speech model that supports dysarthric speech recognition. The performance of the proposed method is evaluated using UA-Speech database. The average accuracy values obtained by the proposed method for speakers with different intelligibility levels with VMD based enhancement and without enhancement are 95.95 and 91.80% respectively. The proposed method also provides an increased accuracy value compared to existing methods that are based on generative models and artificial neural networks.