TY - GEN
T1 - Fractal features for automatic detection of dysarthria
AU - Spangler, Taylor
AU - Vinodchandran, N. V.
AU - Samal, Ashok
AU - Green, Jordan R.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/4/11
Y1 - 2017/4/11
N2 - Amytrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease. Difficulty articulating speech, dysarthria, is a common early symptom of ALS. Detecting dysarthria currently requires manual analysis of several different speech tasks by pathology experts. This is time consuming and can lead to misdiagnosis. Many existing automatic classification approaches require manually preprocessing recordings, separating individual spoken utterances from a repetitive task. In this paper, we propose a fully automated approach which does not rely on manual preprocessing. The proposed method uses novel features based on fractal analysis. Acoustic and associated articulatory recordings of a standard speech diagnostic task, the diadochokinetic test (DDK), are used for classification. This study's experiments show that this approach attains 90.2% accuracy with 94.2% sensitivity and 85.1% specificity.
AB - Amytrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease. Difficulty articulating speech, dysarthria, is a common early symptom of ALS. Detecting dysarthria currently requires manual analysis of several different speech tasks by pathology experts. This is time consuming and can lead to misdiagnosis. Many existing automatic classification approaches require manually preprocessing recordings, separating individual spoken utterances from a repetitive task. In this paper, we propose a fully automated approach which does not rely on manual preprocessing. The proposed method uses novel features based on fractal analysis. Acoustic and associated articulatory recordings of a standard speech diagnostic task, the diadochokinetic test (DDK), are used for classification. This study's experiments show that this approach attains 90.2% accuracy with 94.2% sensitivity and 85.1% specificity.
UR - http://www.scopus.com/inward/record.url?scp=85018458241&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018458241&partnerID=8YFLogxK
U2 - 10.1109/BHI.2017.7897299
DO - 10.1109/BHI.2017.7897299
M3 - Conference contribution
AN - SCOPUS:85018458241
T3 - 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
SP - 437
EP - 440
BT - 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
Y2 - 16 February 2017 through 19 February 2017
ER -