TY - GEN
T1 - Verbal protest recognition in children with autism
AU - Casebeer, Jonah
AU - Sarker, Hillol
AU - Dhuliawala, Murtaza
AU - Fay, Nicholas
AU - Pietrowicz, Mary
AU - Das, Amar
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - Real-time detection of verbal protest (sensory overload-induced crying) in children with autism is a first step towards understanding the precursors of challenging behaviors associated with autism. Detection of verbal protest is useful for both autism researchers interested in exploring just-in-time intervention techniques and researchers interested in audio event detection in routine living environments. In this paper, we examine, adapt, and improve upon two techniques for verbal protest recognition and tailor them for children with autism spectrum disorder (ASD). The first technique investigated is a Gaussian Mixture Model (GMM) with stacking. The second technique uses Convolutional Neural Networks (CNN) trained on log Mel-filter banks (LMFB). We proceed to examine accuracy with a focus on real-world false positive rates and minimization of dataset biases through the introduction of noise and input perturbation.
AB - Real-time detection of verbal protest (sensory overload-induced crying) in children with autism is a first step towards understanding the precursors of challenging behaviors associated with autism. Detection of verbal protest is useful for both autism researchers interested in exploring just-in-time intervention techniques and researchers interested in audio event detection in routine living environments. In this paper, we examine, adapt, and improve upon two techniques for verbal protest recognition and tailor them for children with autism spectrum disorder (ASD). The first technique investigated is a Gaussian Mixture Model (GMM) with stacking. The second technique uses Convolutional Neural Networks (CNN) trained on log Mel-filter banks (LMFB). We proceed to examine accuracy with a focus on real-world false positive rates and minimization of dataset biases through the introduction of noise and input perturbation.
KW - Audio Event Detection
KW - Convolutional Neural Networks
KW - Gaussian Mixture Model
KW - Ubiquitous Computing
UR - http://www.scopus.com/inward/record.url?scp=85054202537&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054202537&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8462514
DO - 10.1109/ICASSP.2018.8462514
M3 - Conference contribution
AN - SCOPUS:85054202537
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 301
EP - 305
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -