Automatic dysfluency detection in dysarthric speech using deep belief networks

Dysarthria is a speech disorder caused by difficulties in controlling muscles, such as the tongue and lips, that are needed to produce speech. These differences in motor skills cause speech to be slurred, mumbled, and spoken relatively slowly, and can also increase the likelihood of dysfluency. This includes nonspeech sounds, and ‘stuttering’, defined here as a disruption in the fluency of speech manifested by prolongations, stop-gaps, and repetitions. This paper investigates different types of input features used by deep neural networks (DNNs) to automatically detect repetition stuttering and non-speech dysfluencies within dysarthric speech. The experiments test the effects of dimensionality within Mel-frequency cepstral coefficients (MFCCs) and linear predictive cepstral coefficients (LPCCs), and explore the detection capabilities in dyarthric versus non-dysarthric speech. The results obtained using MFCC and LPCC features produced similar recognition accuracies; repetition stuttering in dysarthric speech was identified correctly at approximately 86% and 84% for non-dysarthric speech. Non-speech sounds were recognized with approximately 75% accuracy in dysarthric speakers.