- Tytuł:
- GFCC-based x-vectors for Reinke’s edema detection
- Autorzy:
- Kotarba, Katarzyna
- Powiązania:
- https://bibliotekanauki.pl/articles/24202003.pdf
- Data publikacji:
- 2022
- Wydawca:
- Politechnika Poznańska. Instytut Mechaniki Stosowanej
- Tematy:
-
x-vectors
Reinke’s edema
voice pathology classification
x wektory
obrzęk Reinkego
klasyfikacja patologii głosu - Opis:
- Automatic assessment of voice disorders is one of the most important applications of speech signal analysis. Various algorithms utilizing both sustained vowels and continuous speech have been successfully used to perform detection of many voice pathologies, e.g. dysphonia, laryngitis, and vocal folds paralysis. However, algorithms described in literature used for classification of Reinke’s edema - one of the most severe smoking-induced voice conditions - are scarce and rely mostly on speech signals containing sustained vowels. In this paper, a method incorporating gammatone frequency cepstral coefficients (GFCC) based x-vectors extracted from continuous speech is presented. The extracted x-vectors are used to train a SGD classifier performing Reinke’s edema detection. For validation folds, the proposed method yielded AUC ROC, accuracy, recall, and specificity of 0.96 (±0.03), 0.94 (±0.02), 0.92 (±0.03), and 0.94 (±0.02), respectively. For testing set, the method yielded AUC ROC, accuracy, recall, and specificity of 0.98, 0.89, 0.88, and 0.89, respectively.
- Źródło:
-
Vibrations in Physical Systems; 2022, 33, 3; art. no. 2022307
0860-6897 - Pojawia się w:
- Vibrations in Physical Systems
- Dostawca treści:
- Biblioteka Nauki
Artykuł