Utilizing CNN architectures for non-invasive diagnosis of speech disorders – further experiments and insights
This research investigated the application of deep neural networks for diagnosing diseases that affect the voice and speech mechanisms through the non-invasive analysis of vowel sound recordings. Using the Saarbruecken Voice Database, the voice recordings were converted to spectrograms to train the...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2025-07-01
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| Series: | International Journal of Electronics and Telecommunications |
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| Online Access: | https://journals.pan.pl/Content/135742/14_5010_Ratajczak_L_sk.pdf |
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| author | Filip Ratajczak Mikołaj Najda Kamil Szyc |
| author_facet | Filip Ratajczak Mikołaj Najda Kamil Szyc |
| author_sort | Filip Ratajczak |
| collection | DOAJ |
| description | This research investigated the application of deep neural networks for diagnosing diseases that affect the voice and speech mechanisms through the non-invasive analysis of vowel sound recordings. Using the Saarbruecken Voice Database, the voice recordings were converted to spectrograms to train the models, specifically focusing on the vowels /a/, /u/, and /i/. The study used Explainable Artificial Intelligence (XAI) methodologies to identify essential features within these spectrograms for pathology identification, with the aim of providing medical professionals with enhanced insight into how diseases manifest in sound production. The F1 Score performance evaluation showed that the DenseNet model scored 0.70 ± 0.03 with a top of 0.74. The findings indicated that neither vowel selection nor data augmentation strategies significantly improved model performance. Additionally, the research highlighted that signal splitting was ineffective in enhancing the models’ ability to extract features. This study builds on our previous research [1], offering a more comprehensive understanding of the topic. |
| format | Article |
| id | doaj-art-ddaaf5825cd64186b0ee97bd9b3cdeea |
| institution | Kabale University |
| issn | 2081-8491 2300-1933 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj-art-ddaaf5825cd64186b0ee97bd9b3cdeea2025-08-20T03:25:12ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332025-07-01vol. 71No 3https://doi.org/10.24425/ijet.2025.153621Utilizing CNN architectures for non-invasive diagnosis of speech disorders – further experiments and insightsFilip Ratajczak0Mikołaj Najda1Kamil Szyc2Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Wrocław, PolandInstitute of Data Science, Maastricht University, The NetherlandsFaculty of Information and Communication Technology, Wrocław University of Science and Technology, Wrocław, PolandThis research investigated the application of deep neural networks for diagnosing diseases that affect the voice and speech mechanisms through the non-invasive analysis of vowel sound recordings. Using the Saarbruecken Voice Database, the voice recordings were converted to spectrograms to train the models, specifically focusing on the vowels /a/, /u/, and /i/. The study used Explainable Artificial Intelligence (XAI) methodologies to identify essential features within these spectrograms for pathology identification, with the aim of providing medical professionals with enhanced insight into how diseases manifest in sound production. The F1 Score performance evaluation showed that the DenseNet model scored 0.70 ± 0.03 with a top of 0.74. The findings indicated that neither vowel selection nor data augmentation strategies significantly improved model performance. Additionally, the research highlighted that signal splitting was ineffective in enhancing the models’ ability to extract features. This study builds on our previous research [1], offering a more comprehensive understanding of the topic.https://journals.pan.pl/Content/135742/14_5010_Ratajczak_L_sk.pdfvoice disorder diagnosisvowel sound analysisconvolutional neural networks (cnns)explainable artificial intelligence (xai) |
| spellingShingle | Filip Ratajczak Mikołaj Najda Kamil Szyc Utilizing CNN architectures for non-invasive diagnosis of speech disorders – further experiments and insights International Journal of Electronics and Telecommunications voice disorder diagnosis vowel sound analysis convolutional neural networks (cnns) explainable artificial intelligence (xai) |
| title | Utilizing CNN architectures for non-invasive diagnosis of speech disorders – further experiments and insights |
| title_full | Utilizing CNN architectures for non-invasive diagnosis of speech disorders – further experiments and insights |
| title_fullStr | Utilizing CNN architectures for non-invasive diagnosis of speech disorders – further experiments and insights |
| title_full_unstemmed | Utilizing CNN architectures for non-invasive diagnosis of speech disorders – further experiments and insights |
| title_short | Utilizing CNN architectures for non-invasive diagnosis of speech disorders – further experiments and insights |
| title_sort | utilizing cnn architectures for non invasive diagnosis of speech disorders further experiments and insights |
| topic | voice disorder diagnosis vowel sound analysis convolutional neural networks (cnns) explainable artificial intelligence (xai) |
| url | https://journals.pan.pl/Content/135742/14_5010_Ratajczak_L_sk.pdf |
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