Dysfluent Speech Classification Using Variational Mode Decomposition and Complete Ensemble Empirical Mode Decomposition Techniques With NGCU-Based RNN

Dysfluency refers to discontinuity in speech due to noise or speech disorder, this dysfluency has unique features in terms of pitch and time based on these characteristics the dysfluent speech is categorized into repetition, prolongation, or blocking of words or phrases, and because of this uneven structure in speech, it named unstructured speech. The recognition and classification of such unstructured speech require an effective algorithm to boost the process of Automatic Speech Recognition in real-time and it is a challenging task because the system will take more time to extract features like pitch, frequency or time, and energy. From the past few decades, many researchers have proposed algorithms to address the mentioned, but still, there is a gap in recognizing the endpoint of each frame based on the mentioned which helps to robust the recognition accuracy of dysfluent speech. So, in this proposed work to extract the desired and unique features of dysfluent speech the samples are decomposed into different sub-signals by using two advanced methods of Empirical mode decomposition (EMD), they are Variational Mode Decomposition (VMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD). These two techniques overcome the one major disadvantage of EMD that is the EMD techniques use different decomposition levels to produce the oscillation of the same time series signal. Hence combination of VMD and CEEMD decomposes the input speech sample into 5 sub-signals to classify the different classes of dysfluency using a New Gate Control Unit (NGCU) based on Recurrent Neural Networks (RNN).