BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

255 related articles for article (PubMed ID: 26992554)

  • 1. 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]  

  • 2. An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification.
    Al-Nasheri A; Muhammad G; Alsulaiman M; Ali Z; Mesallam TA; Farahat M; Malki KH; Bencherif MA
    J Voice; 2017 Jan; 31(1):113.e9-113.e18. PubMed ID: 27105857
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms.
    Mesallam TA; Farahat M; Malki KH; Alsulaiman M; Ali Z; Al-Nasheri A; Muhammad G
    J Healthc Eng; 2017; 2017():8783751. PubMed ID: 29201333
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. Using modulation spectra for voice pathology detection and classification.
    Markaki M; Stylianou Y
    Annu Int Conf IEEE Eng Med Biol Soc; 2009; 2009():2514-7. PubMed ID: 19964970
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Multidirectional regression (MDR)-based features for automatic voice disorder detection.
    Muhammad G; Mesallam TA; Malki KH; Farahat M; Mahmood A; Alsulaiman M
    J Voice; 2012 Nov; 26(6):817.e19-27. PubMed ID: 23177748
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.
    Fang SH; Tsao Y; Hsiao MJ; Chen JY; Lai YH; Lin FC; Wang CT
    J Voice; 2019 Sep; 33(5):634-641. PubMed ID: 29567049
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. On combining information from modulation spectra and mel-frequency cepstral coefficients for automatic detection of pathological voices.
    Arias-Londoño JD; Godino-Llorente JI; Markaki M; Stylianou Y
    Logoped Phoniatr Vocol; 2011 Jul; 36(2):60-9. PubMed ID: 21073260
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Using Rate of Divergence as an Objective Measure to Differentiate between Voice Signal Types Based on the Amount of Disorder in the Signal.
    Calawerts WM; Lin L; Sprott JC; Jiang JJ
    J Voice; 2017 Jan; 31(1):16-23. PubMed ID: 26920858
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Validation of the Acoustic Voice Quality Index in the Japanese Language.
    Hosokawa K; Barsties B; Iwahashi T; Iwahashi M; Kato C; Iwaki S; Sasai H; Miyauchi A; Matsushiro N; Inohara H; Ogawa M; Maryn Y
    J Voice; 2017 Mar; 31(2):260.e1-260.e9. PubMed ID: 27287930
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Maximal Ambient Noise Levels and Type of Voice Material Required for Valid Use of Smartphones in Clinical Voice Research.
    Lebacq J; Schoentgen J; Cantarella G; Bruss FT; Manfredi C; DeJonckere P
    J Voice; 2017 Sep; 31(5):550-556. PubMed ID: 28320627
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Discrimination of pathological voices using a time-frequency approach.
    Umapathy K; Krishnan S; Parsa V; Jamieson DG
    IEEE Trans Biomed Eng; 2005 Mar; 52(3):421-30. PubMed ID: 15759572
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Teachers' Perception of Vocal Quality Compared With Professional Perception.
    Selevan E; Schorr E; Pekarsky R; Mitta S; Diamont S; Stept E; Oliveira G
    J Voice; 2016 Nov; 30(6):763.e17-763.e21. PubMed ID: 26739856
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Concatenation of the Moving Window Technique for Auditory-Perceptual Analysis of Voice Quality.
    Ehrlich B; Lin L; Jiang J
    Am J Speech Lang Pathol; 2018 Nov; 27(4):1426-1433. PubMed ID: 30304342
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An Acoustic-Signal-Based Preventive Program for University Lecturers' Vocal Health.
    Paniagua MS; Pérez CJ; Calle-Alonso F; Salazar C
    J Voice; 2020 Jan; 34(1):88-99. PubMed ID: 30072204
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.