Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features
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 col...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9096347/ |
| _version_ | 1818857110913417216 |
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| author | Andrius Lauraitis Rytis Maskeliunas Robertas Damasevicius Tomas Krilavicius |
| author_facet | Andrius Lauraitis Rytis Maskeliunas Robertas Damasevicius Tomas Krilavicius |
| author_sort | Andrius Lauraitis |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-12-19T08:35:11Z |
| format | Article |
| id | doaj.art-3aff503b430245d5bb8c54c2e48de261 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:35:11Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3aff503b430245d5bb8c54c2e48de2612022-12-21T20:29:03ZengIEEEIEEE Access2169-35362020-01-018961629617210.1109/ACCESS.2020.29957379096347Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain FeaturesAndrius Lauraitis0Rytis Maskeliunas1Robertas Damasevicius2https://orcid.org/0000-0001-9990-1084Tomas Krilavicius3Department of Multimedia Engineering, Kaunas University of Technology, KaunasLithuaniaDepartment of Applied Informatics, Vytautas Magnus University, Kaunas, LithuaniaDepartment of Applied Informatics, Vytautas Magnus University, Kaunas, LithuaniaDepartment of Applied Informatics, Vytautas Magnus University, Kaunas, LithuaniaWe 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).https://ieeexplore.ieee.org/document/9096347/Neural impairmentmobile appdeep learningwavelet scatteringdecision supportspeech processing |
| spellingShingle | Andrius Lauraitis Rytis Maskeliunas Robertas Damasevicius Tomas Krilavicius Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features IEEE Access Neural impairment mobile app deep learning wavelet scattering decision support speech processing |
| title | Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features |
| title_full | Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features |
| title_fullStr | Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features |
| title_full_unstemmed | Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features |
| title_short | Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features |
| title_sort | detection of speech impairments using cepstrum auditory spectrogram and wavelet time scattering domain features |
| topic | Neural impairment mobile app deep learning wavelet scattering decision support speech processing |
| url | https://ieeexplore.ieee.org/document/9096347/ |
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