An Evolving Model of Voice Disorder Detection using Deep Belief Network
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2019 by IJCTT Journal|
|Year of Publication : 2019|
|Authors : P. Kokila, Dr. G. M. Nasira|
|DOI : 10.14445/22312803/IJCTT-V67I8P105|
MLA Style:P. Kokila, Dr. G. M. NasiraAn Evolving Model of Voice Disorder Detection using Deep Belief Network" International Journal of Computer Trends and Technology 67.8 (2019):20-25.
APA Style P. Kokila, Dr. G. M. Nasira. An Evolving Model of Voice Disorder Detection using Deep Belief NetworkInternational Journal of Computer Trends and Technology, 67(8),20-25.
In recent years, automatic diagnose of larynx pathological voice disorders are a challenging task in the medical filed. The researchers started focusing on working with voice signals to discover voice disorder related diseases. Machine learning plays a vital role in automatic detection of voice disorder using spectral information of recorded voice. Among several approaches deep playing has been in a prominent place for achieving significant results in the voice recognition field, where there has been less research work in the field of pathological voice detection. This paper introduces the deep belief network for discovering healthy and unhealthy voice detection. The stack of Restricted Boltzmann Machine is used to pretrain the deep neural networks. Simulation analysis is done to prove the proficiency of the deep belief network-based voice disorder detection using the real data from the Saarbrucken Voice database..
 K. Verdolini and L. O. Ramig, "Occupational risks for voice problems," Logopedics Phoniatrics Vocology, vol. 26, no. 1, pp. 37-46, 2001.
  A. A. Dibazar, S. Narayanan, and T. W. Berger, "Feature analysis for automatic detection of pathological speech," in Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, Engineering in Medicine and Biology, 2002, vol. 1, pp. 182-183 vol.1.
 M. K. Arjmandi and M. Pooyan, "An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine," Biomedical Signal Processing and Control, vol. 7, no1, pp. 3-19, 2012/01/01/ 2012.
 M. Hariharan, K. Polat, and S. Yaacob, "A new feature constituting approach to detection of vocal fold pathology,"International Journal of Systems Science, vol. 45, no. 8, pp. 1622-1634, 2014/08/03 2014
 A. Al-nasheri et al., "An Investigation of Multidimensional VoiceProgram Parameters in Three Different Databases for VoicePathology Detection and Classification," Journal of Voice, vol.31, no. 1, pp. 113.e9-113.e18.
 G. Muhammad et al., "Voice pathology detection using interlacedderivative pattern on glottal source excitation," Biomedical SignalProcessing and Control, vol. 31, pp. 156-164, 2017/01/01/ 2017.
 G. E. Hinton, S. Osindero, Y. Teh, “A fast learning algorithm for deep belief nets.,” Neural Comput., vol. 18, pp. 1527–1554, 2006.
 A. Kae, K. Sohn, H. Lee, E.Learned-Miller, “Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling.,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
 Maria Markaki, Yannis Stylianou,Voice Pathology Detection and Discrimination Based on Modulation SpectralFeatures, IEEE Transactions on Audio Speech and Language Processing, pp 1-12, October 2011
 N. Malyska, T. Quatieri, and D. Sturim, “Automatic dysphonia recognition using biologically inspired amplitude-modulation features,” in Proc.ICASSP, 2005, pp. 873–876.
 Markaki, M., Stylianou, Y. Voice Pathology Detection and Discrimination Based on Modulation Spectral Features. IEEETransactions on Audio, Speech, and Language Processing, Vol. 19, No. 7, 1938-1948, 2011.
 Panek, D., Skalski, A., Gajda, J., Tadeusiewicz, R. Acoustic Analysis Assessment in Speech Pathology Detection. Int. J. Appl. Math. Comput. Sci., 2015, Vol. 25, No. 3, 631–643.
 Al-nasheri, A., Muhammad, G., Alsulaiman, M., Ali, Z. Investigation of Voice Pathology Detection and Classification on Different Frequency Regions Using Correlation Functions. Journal of Voice, 31(1):3-15, 2016
 Cordeiro, Hugo T. Reconhecimento de Patologias da VozusandoTécnicas de Processamento da Fala. PhD thesis at Universidade Nova de Lisboa, 2016.
 Barry, W.J., Pützer, M. Saarbrücken Voice Database, Institute of Phonetics, Univ. of Saarland, http://www.stimmdatenbank.coli.unisaarland.de/
Voice disorder, deep learning, deep belief network, Restricted Boltzmann Machine, pathological voice, machine learning