Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "voice pathology" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-3 z 3
    Tytuł:
    Acoustic analysis assessment in speech pathology detection
    Autorzy:
    Panek, D.
    Skalski, A.
    Gajda, J.
    Tadeusiewicz, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/329710.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    linear PCA
    nonlinear PCA
    autoassociative neural network
    validation
    voice pathology detection
    Opis:
    Automatic detection of voice pathologies enables non-invasive, low cost and objective assessments of the presence of disorders, as well as accelerating and improving the process of diagnosis and clinical treatment given to patients. In this work, a vector made up of 28 acoustic parameters is evaluated using principal component analysis (PCA), kernel principal component analysis (kPCA) and an auto-associative neural network (NLPCA) in four kinds of pathology detection (hyperfunctional dysphonia, functional dysphonia, laryngitis, vocal cord paralysis) using the a, i and u vowels, spoken at a high, low and normal pitch. The results indicate that the kPCA and NLPCA methods can be considered a step towards pathology detection of the vocal folds. The results show that such an approach provides acceptable results for this purpose, with the best efficiency levels of around 100%. The study brings the most commonly used approaches to speech signal processing together and leads to a comparison of the machine learning methods determining the health status of the patient.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2015, 25, 3; 631-643
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Voice pathology assessment using x-vectors approach
    Autorzy:
    Kotarba, Katarzyna
    Kotarba, Michał
    Powiązania:
    https://bibliotekanauki.pl/articles/2146638.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Poznańska. Instytut Mechaniki Stosowanej
    Tematy:
    x-vectors
    speaker embeddings
    voice pathology
    MFCC
    GFCC
    x wektory
    osadzenie głośnika
    patologia głosu
    Opis:
    Voice pathology assessment using sustained vowels has proven to be effective and reliable. However, only a few studies regarding detection of pathological speech based on continuous speech are available. In this study we evaluate the usefulness of various regression models trained on continuous speech recordings from Saarbruecken Voice Database in the detection of voice pathologies. The recordings were used for extraction of speaker embeddings called x-vectors based on mel-frequency cepstral coefficients and gammatone frequency cepstral coefficients. Since the dataset used in this study is imbalanced, various over- and undersampling techniques were applied to the training set to ensure robustness of models’ decision boundaries. The models were trained on both imbalanced and resampled training sets using 5-fold cross-validation. The best results were obtained for Multi Layer Perceptron trained on GFCC-based x-vectors, achieving accuracy of 0.8184, F1-score of 0.8212, and ROC AUC score of 0.8810 for the testing set.
    Źródło:
    Vibrations in Physical Systems; 2021, 32, 1; art. no. 2021108
    0860-6897
    Pojawia się w:
    Vibrations in Physical Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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
      Wyświetlanie 1-3 z 3

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies