Assessment Of The Intelligibility Of Dysarthria Speech Using Acoustic And Articulatory Characteristics

Dysarthria is a motor nervous system disorder that affects the speech articulator movements as well as the intelligibility of speech sound.There are only a few studies that investigate automatic diagnosis of dysarthria intelligibility from the articulator movement aspects.Besides,dysarthric patients cannot provide large amounts of data to analyze,especially for the severer patients.Few study attempt to assess the dysarthria using few data.In recording Electromagnetic Articulograph(EMA)data,however,abnormal tongue movements of dysarthria cause the sensors on the tongue falling off,so that it is quite difficult to monitor.For this reason,in this study,lip articulatory data is adopted to analyze.By contrast,the MFCC feature and lip articulatory features are extract from the continuous speech utterances.The lip articulatory feature can easily distinguish different group through the visualization of two kinds of features.Meanwhile,both feature fusion method and decision fusion method are applied to the acoustic feature and the lip articulatory features.In our experiment,the Gaussian Mixture Model-Hidden Markov Model(GMM-HMM)is utilized to train the dysarthria assessment model of four severity levels.The lip articulatory features-based approach outperformed acoustic featurebased approaches.The results verified the efficiency of lip articulatory features.In addition,the performance of objective intelligibility assessment was further improved by using a feature fusion method and a decision fusion method.The proposed feature fusion method achieved a relative error reduction rate of 77.1% over the acoustic feature-based method.Moreover,linear discriminant analysis(LDA)is applied to lip articulatory feature to extract discriminative and low-dimensional articulator feature(articulatory feature-LDA).The proposed articulatory feature achieves better intelligibility assessment performance than the original articulatory feature and acoustic feature with small training data and different severity of dysarthria.