Improving dysarthric speech recognition using empirical mode decomposition and convolutional neural network

摘要

In this paper, we use empirical mode decomposition and Hurst-based mode selection (EMDH) along with deeplearning architecture using a convolutional neural network (CNN) to improve the recognition of dysarthric speech. TheEMDH speech enhancement technique is used as a preprocessing step to improve the quality of dysarthric speech.Then, the Mel-frequency cepstral coefficients are extracted from the speech processed by EMDH to be used as inputfeatures to a CNN-based recognizer. The effectiveness of the proposed EMDH-CNN approach is demonstrated by theresults obtained on the Nemours corpus of dysarthric speech. Compared to baseline systems that use Hidden Markovwith Gaussian Mixture Models (HMM-GMMs) and a CNN without an enhancement module, the EMDH-CNN systemincreases the overall accuracy by 20.72% and 9.95%, respectively, using a k-fold cross-validation experimental setup.