| Title |
Detection of speech impairments using cepstrum, auditory spectrogram and wavelet time scattering domain features / |
| Authors |
Lauraitis, Andrius ; Maskeliūnas, Rytis ; Damaševičius, Robertas ; Krilavičius, Tomas |
| DOI |
10.1109/ACCESS.2020.2995737 |
| Full Text |
|
| Is Part of |
IEEE Access.. Piscataway, NJ : IEEE. 2020, vol. 8, p. 96162-96172.. ISSN 2169-3536 |
| Keywords [eng] |
decision support ; deep learning ; digital health ; Internet of medical things ; mobile app ; neural impairment ; speech processing ; wavelet scattering |
| Abstract [eng] |
We adopt Bidirectional Long Short-Term Memory (BiLSTM) neural network and Wavelet Scattering Transform with Support Vector Machine (WST-SVM) classifier for detecting speech impairments of patients at the early stage of central nervous system disorders (CNSD). The study includes 339 voice samples collected from 15 subjects: 7 patients with early stage CNSD (3 Huntington, 1 Parkinson, 1 cerebral palsy, 1 post stroke, 1 early dementia), other 8 subjects were healthy. Speech data is collected using voice recorder from Neural Impairment Test Suite (NITS) mobile app. Features are extracted from pitch contours, Mel-frequency cepstral coefficients (MFCC), Gammatone cepstral coefficients (GTCC), Gabor (analytic Morlet) wavelet and auditory spectrograms. 94.50% (BiLSTM) and 96.3% (WST-SVM) accuracy is achieved for solving healthy vs. impaired classification problem. The developed method can be applied for automated CNSD patient health state monitoring and clinical decision support systems as well as a part of Internet of Medical Things (IoMT). |
| Published |
Piscataway, NJ : IEEE |
| Type |
Journal article |
| Language |
English |
| Publication date |
2020 |
| CC license |
|
