Voice Disorder Recognition and Analysis Using Knowledge Engineering Techniques

  • Vengateshwaran M Assistant Professor , Department of CSE, Arasu Engineering College, Kumbakonam,TN,India
  • Gowsalya N UG Student , Department of CSE, Arasu Engineering College, Kumbakonam, TN, India
  • Atchaya K UG Student , Department of CSE, Arasu Engineering College, Kumbakonam, TN, India
  • Nivetha R UG Student , Department of CSE, Arasu Engineering College, Kumbakonam, TN, India
Keywords: Mobile health systems, machine learning techniques, voice disorders, accuracy


Nowadays, the use of mobile application is most important thing in the healthcare sector is increasing rapidly. Mobile technologies not only for communication for multimedia content (e.g. clinical audio-visual notes and medical records) but also promising solutions for people who desire the identification, monitoring, and treatment of their health conditions anywhere and at any time. Mobile E-healthcare systems can contribute to make patient care faster, better, and cheaper. Several pathological conditions can benefit from the use of mobile technologies. In this paper we focus on dysphonia, an alteration of the voice quality that affects about one person in three at least once in his/her lifetime. Voice disorders are rapidly spreading, although they are often underestimated. Mobile health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is necessary to realize a valid and precise mobile health system. . This technique is evaluated by based on experimental results deep neural networks with machine learning approach to provide an accuracy of 99.89% in detecting voice. In this field to detect any abnormal structure and analysis without human intervention in health care sector to enhance the utility of well beginning system.


[1]. B. M. C. Silva, J. J. C. Rodrigues, I. de la Torre Díez, M. López-Coronado, and K. Saleem, ‘‘Mobile-health: A review of current state in 2015,’’ J. Biomed. Inform., vol. 56, pp. 265–272, Aug. 2015.
[2]. S. Naddeo, L. Verde, M. Forastiere, G. De Pietro, and G. Sannino, ‘‘A real-time m-health monitoring system: An integrated solution combining the use of several wearable sensors and mobile devices,’’ in Proc. HEALTHINF, 2017, pp. 545–552.
[3]. M. S. Hossain and G. Muhammad, ‘‘Cloud-assisted Industrial Internet of Things (IIoT)—Enabled framework for health monitoring,’’ Comput. Netw., vol. 101, pp. 192–202, Jun. 2016.
[4]. G. Sannino, I. De Falco, and G. De Pietro, ‘‘A supervised approach to automatically extract a set of rules to support fall detection in an mHealth system,’’ Appl. Soft Comput., vol. 34, pp. 205–216, Sep. 2015.
[5]. J. Mohammed, C.-H. Lung, A. Ocneanu, A. Thakral, C. Jones, and A. Adler, ‘‘Internet of Things: Remote patient monitoring using Web services and cloud computing,’’ in Proc. IEEE Int. Conf. Green Comput. Commun. (GreenCom), IEEE Cyber, Phys. Social Comput. (CPSCom), Internet Things (iThings), Sep. 2014, pp. 256–263.
[6]. R. Gravina and G. Fortino, ‘‘Automatic methods for the detection of accelerative cardiac defense response,’’ IEEE Trans. Affect. Comput., vol. 7, no. 3, pp. 286–298, Jul./Sep. 2016.
[7]. S. Iyengar, F. T. Bonda, R. Gravina, A. Guerrieri, G. Fortino, and A. Sangiovanni-Vincentelli, ‘‘A framework for creating healthcare monitoring applications using wireless body sensor networks,’’ in Proc. ICST 3rd Int. Conf. Body Area Network. (ICST), 2008, p. 8.
[8]. R. H. G. Martins, H. A. do Amaral, E. L. M. Tavares, M. G. Martins, T. M. Conclaves, and N. H. Dias, ‘‘Voice disorders: Etiology and diagnosis,’’ J. Voice, vol. 30, no. 6, pp. 761.e1–761.e9, Nov. 2016.
How to Cite
M, V., N, G., K, A., & R, N. (2019). Voice Disorder Recognition and Analysis Using Knowledge Engineering Techniques. International Research Journal of Multidisciplinary Technovation, 1(5), 1-7. Retrieved from https://mapletreejournals.com/index.php/irjmt/article/view/249