Combined Method for Informative Feature Selection for Speech Pathology Detection
The task of detecting vocal abnormalities is characterized by a small amount of available data for training, as a consequence of which classification systems that use low-dimensional data are the most relevant. We propose to use LASSO (least absolute shrinkage and selection operator) and BSS (backwa...
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| Format: | Article |
| Language: | Russian |
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Educational institution «Belarusian State University of Informatics and Radioelectronics»
2023-08-01
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| Series: | Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki |
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| Online Access: | https://doklady.bsuir.by/jour/article/view/3689 |
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| _version_ | 1849240537848086528 |
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| author | D. S. Likhachov M. I. Vashkevich N. A. Petrovsky E. S. Azarov |
| author_facet | D. S. Likhachov M. I. Vashkevich N. A. Petrovsky E. S. Azarov |
| author_sort | D. S. Likhachov |
| collection | DOAJ |
| description | The task of detecting vocal abnormalities is characterized by a small amount of available data for training, as a consequence of which classification systems that use low-dimensional data are the most relevant. We propose to use LASSO (least absolute shrinkage and selection operator) and BSS (backward stepwise selection) methods together to select the most significant features for the detection of vocal pathologies, in particular amyotrophic lateral sclerosis. Features based on fine-frequency cepstral coefficients, traditionally used in speech signal processing, and features based on discrete estimation of the autoregressive spectrum envelope are used. Spectral features based on the autoregressive process envelope spectrum are extracted using the generative method, which involves calculating a discrete Fourier transform of the report sequence generated using the autoregressive model of the input voice signal. The sequence is generated by the autoregressive model so as to account for the periodic nature of the Fourier transform. This improves the accuracy of the spectrum estimation and reduces the spectral leakage effect. Using LASSO in conjunction with BSS allowed us to improve the classification efficiency using a smaller number of features as compared to using the LASSO method alone. |
| format | Article |
| id | doaj-art-adc86e638e1b4ae4b317c0a2c504a89c |
| institution | Kabale University |
| issn | 1729-7648 |
| language | Russian |
| publishDate | 2023-08-01 |
| publisher | Educational institution «Belarusian State University of Informatics and Radioelectronics» |
| record_format | Article |
| series | Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki |
| spelling | doaj-art-adc86e638e1b4ae4b317c0a2c504a89c2025-08-20T04:00:33ZrusEducational institution «Belarusian State University of Informatics and Radioelectronics»Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki1729-76482023-08-0121411011710.35596/1729-7648-2023-21-4-110-1171928Combined Method for Informative Feature Selection for Speech Pathology DetectionD. S. Likhachov0M. I. Vashkevich1N. A. Petrovsky2E. S. Azarov3Belarusian State University of Informatics and RadioelectronicsBelarusian State University of Informatics and RadioelectronicsBelarusian State University of Informatics and RadioelectronicsBelarusian State University of Informatics and RadioelectronicsThe task of detecting vocal abnormalities is characterized by a small amount of available data for training, as a consequence of which classification systems that use low-dimensional data are the most relevant. We propose to use LASSO (least absolute shrinkage and selection operator) and BSS (backward stepwise selection) methods together to select the most significant features for the detection of vocal pathologies, in particular amyotrophic lateral sclerosis. Features based on fine-frequency cepstral coefficients, traditionally used in speech signal processing, and features based on discrete estimation of the autoregressive spectrum envelope are used. Spectral features based on the autoregressive process envelope spectrum are extracted using the generative method, which involves calculating a discrete Fourier transform of the report sequence generated using the autoregressive model of the input voice signal. The sequence is generated by the autoregressive model so as to account for the periodic nature of the Fourier transform. This improves the accuracy of the spectrum estimation and reduces the spectral leakage effect. Using LASSO in conjunction with BSS allowed us to improve the classification efficiency using a smaller number of features as compared to using the LASSO method alone.https://doklady.bsuir.by/jour/article/view/3689voice analysisgenerative methodautoregressionmachine learningspectral featuresclassification |
| spellingShingle | D. S. Likhachov M. I. Vashkevich N. A. Petrovsky E. S. Azarov Combined Method for Informative Feature Selection for Speech Pathology Detection Doklady Belorusskogo gosudarstvennogo universiteta informatiki i radioèlektroniki voice analysis generative method autoregression machine learning spectral features classification |
| title | Combined Method for Informative Feature Selection for Speech Pathology Detection |
| title_full | Combined Method for Informative Feature Selection for Speech Pathology Detection |
| title_fullStr | Combined Method for Informative Feature Selection for Speech Pathology Detection |
| title_full_unstemmed | Combined Method for Informative Feature Selection for Speech Pathology Detection |
| title_short | Combined Method for Informative Feature Selection for Speech Pathology Detection |
| title_sort | combined method for informative feature selection for speech pathology detection |
| topic | voice analysis generative method autoregression machine learning spectral features classification |
| url | https://doklady.bsuir.by/jour/article/view/3689 |
| work_keys_str_mv | AT dslikhachov combinedmethodforinformativefeatureselectionforspeechpathologydetection AT mivashkevich combinedmethodforinformativefeatureselectionforspeechpathologydetection AT napetrovsky combinedmethodforinformativefeatureselectionforspeechpathologydetection AT esazarov combinedmethodforinformativefeatureselectionforspeechpathologydetection |