These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
113 related articles for article (PubMed ID: 37857135)
1. Unraveling the complexities of pathological voice through saliency analysis. Shaikh AAS; Bhargavi MS; Naik GR Comput Biol Med; 2023 Nov; 166():107566. PubMed ID: 37857135 [TBL] [Abstract][Full Text] [Related]
2. The Effect of the MFCC Frame Length in Automatic Voice Pathology Detection. Tirronen S; Kadiri SR; Alku P J Voice; 2024 Sep; 38(5):975-982. PubMed ID: 35490081 [TBL] [Abstract][Full Text] [Related]
3. Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features. Eskidere Ö; Gürhanlı A Comput Math Methods Med; 2015; 2015():956249. PubMed ID: 26681977 [TBL] [Abstract][Full Text] [Related]
4. Investigation of Voice Pathology Detection and Classification on Different Frequency Regions Using Correlation Functions. Al-Nasheri A; Muhammad G; Alsulaiman M; Ali Z J Voice; 2017 Jan; 31(1):3-15. PubMed ID: 26992554 [TBL] [Abstract][Full Text] [Related]
5. An Analytical Study of Speech Pathology Detection Based on MFCC and Deep Neural Networks. Zakariah M; B R; Ajmi Alotaibi Y; Guo Y; Tran-Trung K; Elahi MM Comput Math Methods Med; 2022; 2022():7814952. PubMed ID: 35529259 [TBL] [Abstract][Full Text] [Related]
6. Deep Neural Network for Automatic Classification of Pathological Voice Signals. Chen L; Chen J J Voice; 2022 Mar; 36(2):288.e15-288.e24. PubMed ID: 32660846 [TBL] [Abstract][Full Text] [Related]
7. Deep learning in automatic detection of dysphonia: Comparing acoustic features and developing a generalizable framework. Chen Z; Zhu P; Qiu W; Guo J; Li Y Int J Lang Commun Disord; 2023 Mar; 58(2):279-294. PubMed ID: 36117378 [TBL] [Abstract][Full Text] [Related]
8. Convolutional Neural Networks for Pathological Voice Detection. Wu H; Soraghan J; Lowit A; Di Caterina G Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():1-4. PubMed ID: 30440307 [TBL] [Abstract][Full Text] [Related]
9. Voice pathology detection and classification from speech signals and EGG signals based on a multimodal fusion method. Geng L; Shan H; Xiao Z; Wang W; Wei M Biomed Tech (Berl); 2021 Dec; 66(6):613-625. PubMed ID: 34845886 [TBL] [Abstract][Full Text] [Related]
10. Hierarchical Classification and System Combination for Automatically Identifying Physiological and Neuromuscular Laryngeal Pathologies. Cordeiro H; Fonseca J; Guimarães I; Meneses C J Voice; 2017 May; 31(3):384.e9-384.e14. PubMed ID: 27743845 [TBL] [Abstract][Full Text] [Related]
11. Robustness of auditory Teager Energy Cepstrum Coefficients for classification of pathological and normal voices in noisy environments. Salhi L; Cherif A ScientificWorldJournal; 2013; 2013():435729. PubMed ID: 23818821 [TBL] [Abstract][Full Text] [Related]
12. Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks. Semmad A; Bahoura M Comput Biol Med; 2024 Mar; 171():108190. PubMed ID: 38387384 [TBL] [Abstract][Full Text] [Related]
13. CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals. Celik G Comput Biol Med; 2023 Sep; 163():107153. PubMed ID: 37321101 [TBL] [Abstract][Full Text] [Related]
14. Design and Validation of a New Diagnostic Tool for the Differentiation of Pathological Voices in Parkinsonian Patients. Almaloglou EEI; S G; Chrousos G; K K Adv Exp Med Biol; 2021; 1339():77-83. PubMed ID: 35023093 [TBL] [Abstract][Full Text] [Related]
15. Automatic Voice Pathology Detection With Running Speech by Using Estimation of Auditory Spectrum and Cepstral Coefficients Based on the All-Pole Model. Ali Z; Elamvazuthi I; Alsulaiman M; Muhammad G J Voice; 2016 Nov; 30(6):757.e7-757.e19. PubMed ID: 26522263 [TBL] [Abstract][Full Text] [Related]
16. Analysis and Classification of Voice Pathologies Using Glottal Signal Parameters. Forero M LA; Kohler M; Vellasco MM; Cataldo E J Voice; 2016 Sep; 30(5):549-56. PubMed ID: 26474715 [TBL] [Abstract][Full Text] [Related]
17. Intra- and Inter-database Study for Arabic, English, and German Databases: Do Conventional Speech Features Detect Voice Pathology? Ali Z; Alsulaiman M; Muhammad G; Elamvazuthi I; Al-Nasheri A; Mesallam TA; Farahat M; Malki KH J Voice; 2017 May; 31(3):386.e1-386.e8. PubMed ID: 27745756 [TBL] [Abstract][Full Text] [Related]
18. Discrimination between pathological and normal voices using GMM-SVM approach. Wang X; Zhang J; Yan Y J Voice; 2011 Jan; 25(1):38-43. PubMed ID: 20137892 [TBL] [Abstract][Full Text] [Related]
19. Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors. Godino-Llorente JI; Gómez-Vilda P IEEE Trans Biomed Eng; 2004 Feb; 51(2):380-4. PubMed ID: 14765711 [TBL] [Abstract][Full Text] [Related]
20. Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis. Kim HB; Song J; Park S; Lee YO Sci Rep; 2024 Apr; 14(1):9297. PubMed ID: 38654036 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]