Dysfluency Classification in Speech Using a Biological Sound Perception Model

Dysfluency classification for stuttered speech has been tackled from different perspectives over the years, with research being more and more focused on deep learning. Here, we use a specific biological model of sound texture perception to extract a subband representation of speech and statistical features. A statistical analysis was also performed to identify relevant features. Afterwards, dysfluency classification was performed using a Random Forest Classifier to perform multi-label classification on the FluencyBank dataset and Support Vector Machine on the UCLASS dataset. This method performs as well or better than current state of the art deep learning algorithm, suggesting that approaching speech classification problems from a more biological point of view is a promising direction.