TY - GEN
T1 - A Novel Stuttering Disfluency Classification System Based on Respiratory Biosignals
AU - Villegas, Bruno
AU - Flores, Kevin M.
AU - Jose Acuna, Kevin
AU - Pacheco-Barrios, Kevin
AU - Elias, Dante
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Stuttering is the principal fluency disorder that affects 1% of the world population. Growing with this disorder can impact the quality of life of the adults who stutter (AWS). To manage this condition, it is necessary to measure and assess the stuttering severity before, during and after any therapeutic process. The respiratory biosignal activity could be an option for automatic stuttering assessment, however, there is not enough evidence of its use for this purposes. Thus, the aim of this research is to develop a stuttering disfluency classification system based on respiratory biosignals. Sixty-eight participants (training: AWS=27, AWNS=33; test: AWS=9) were asked to perform a reading task while their respiratory patterns and pulse were recorded through a standardized system. Segmentation, feature extraction and Multilayer Perceptron Neural Network (MLP) was implemented to differentiate block and non-block states based on the respiratory biosignal activity. 82.6% of classification accuracy was obtained after training and testing the neural network. This work presents an accurate system to classify block and non-block states of speech from AWS during reading tasks. It is a promising system for future applications such as screening of stuttering, monitoring and biofeedback interventions.
AB - Stuttering is the principal fluency disorder that affects 1% of the world population. Growing with this disorder can impact the quality of life of the adults who stutter (AWS). To manage this condition, it is necessary to measure and assess the stuttering severity before, during and after any therapeutic process. The respiratory biosignal activity could be an option for automatic stuttering assessment, however, there is not enough evidence of its use for this purposes. Thus, the aim of this research is to develop a stuttering disfluency classification system based on respiratory biosignals. Sixty-eight participants (training: AWS=27, AWNS=33; test: AWS=9) were asked to perform a reading task while their respiratory patterns and pulse were recorded through a standardized system. Segmentation, feature extraction and Multilayer Perceptron Neural Network (MLP) was implemented to differentiate block and non-block states based on the respiratory biosignal activity. 82.6% of classification accuracy was obtained after training and testing the neural network. This work presents an accurate system to classify block and non-block states of speech from AWS during reading tasks. It is a promising system for future applications such as screening of stuttering, monitoring and biofeedback interventions.
KW - Stuttering
KW - biosignals processing
KW - classification
KW - respiratory patterns
UR - http://www.scopus.com/inward/record.url?scp=85077880799&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8857891
DO - 10.1109/EMBC.2019.8857891
M3 - Conference contribution
C2 - 31946902
AN - SCOPUS:85077880799
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4660
EP - 4663
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -