- Volume 13 Issue 12
DOI QR Code
Development of medical/electrical convergence software for classification between normal and pathological voices
장애 음성 판별을 위한 의료/전자 융복합 소프트웨어 개발
- Moon, Ji-Hye (Department of Biomedical Engineering, Jungwon University) ;
- Lee, JiYeoun (Department of Biomedical Engineering, Jungwon University)
- Received : 2015.10.30
- Accepted : 2015.12.20
- Published : 2015.12.28
If the software is developed to analyze the speech disorder, the application of various converged areas will be very high. This paper implements the user-friendly program based on CART(Classification and regression trees) analysis to distinguish between normal and pathological voices utilizing combination of the acoustical and HOS(Higher-order statistics) parameters. It means convergence between medical information and signal processing. Then the acoustical parameters are Jitter(%) and Shimmer(%). The proposed HOS parameters are means and variances of skewness(MOS and VOS) and kurtosis(MOK and VOK). Database consist of 53 normal and 173 pathological voices distributed by Kay Elemetrics. When the acoustical and proposed parameters together are used to generate the decision tree, the average accuracy is 83.11%. Finally, we developed a program with more user-friendly interface and frameworks.
Higher-order Statistics;Acoustical analysis;Convergence voice analysis software;Biomedical electricity
Supported by : 한국연구재단
- Jinsu Lee, KHIDI Brief vol.140, pp.1-2, Korea Health Industry Development Institute, 2014.
- Hwa-Young Pyo; Hyun Sub Sim, A Study for the Development of Korean Voice Assessment Model for the Patients with Voice Disorders: A Qualitative Study, The Korean Association of Speech Sciences, vol. 14, no.2, pp. 7-22 (16 pages), 2007.
- Ji-Yeoun Lee; Minsoo Hahn, Automatic Assessment of Pathological Voice Quality Using Higher-Order Statistics in the LPC Residual Domain, EURASIP Journal on Advances in Signal Processing, Volume 2009, Article ID 748207, 8 pages, 2009.
- Soon-Bok Kwon; Soon-Woo Kwon, The Effect of Self Voice Feedback Training Using Praat on the Voice Improvement of Patient with Vocal Nodules, Journal of Special Education & Rehabilitation Science, Vol. 46, No. 1, pp. 191-215, 2007.
- J.B. Alonso et al., "Automatic Detection of Pathologies in the Voice by HOS Based Parameters," EURASIP Journal on Applied Signal Processing, vol. 4, pp. 275-284, 2001.
- Ki-Chang Nam; Seung-Hoon Lee; Jai-Nam Choi; Hong-Shik Choi; Do-Hyun Nam; Deok-Won Kim, Comparison of vowel pitch results among several commercial voice analysis programs, ICS'05, pp.54-56, 2005.
- Ji-Yeoun Lee, Performance Improvement of Automatic Pathological Voice Quality Assessment Based on Higher-Order Statistics, ICU-Schoo of Engineering [Thesis(doctoral)], pp.109, 2008.
- Bong-Hyun Kim; Dong-Uk Cho, Pronunciation Influence Analysis of Carbonate Drink and Eucalyptus Fragrance by Applying Speech Signal Processing Techniques, The Journal of Korean Institute of Communications and Information Sciences, Volume 37, Issue 5C, pp.420-428, 2012.
- Taeyeong Shin; Giseong Kim; Yeonguk Kwon; Hyeongsun Kim, Speaker Identification based on Higher-Order Satistics in Noisy Environment, The journal of the acoustical society of Korea, v.16, no.6, pp. 25-35, 1997.
- Tae Young Shin;Jae Ho Kim; Kyung Sik Son; Hyung Soon Kim, Pitch Determination and Voiced/Unvoiced Decision of Noisy Speech Based on the Higher-Order Statistis, SCAS, Vol. 12, no. 1, 1995.
- JiYeoun Lee; Seong Hee Cho,. Perturbation analysis using a moving window for disordered voices, International Journal of Engineering, Science and Innovative Technology, Vol. 3, No. 1, pp. 1-10, 2012.
- Kay Elemetrics Corp. Multi-dimensional voice program: software instruction manual. Pine Brook: NJ: Kay Elemetrics Corp, 1993.
- J.I. Godino-Llorente; N. Saenz-Lechon; V. Osma-Ruiz; S. Aguilera-Navarro; P. Gomez-Vilda, An integrated tool for the diagnosis of voice disorders, Medical Engineering & Physics, Vol. 28, No. 3, pp. 276-289, 2006. https://doi.org/10.1016/j.medengphy.2005.04.014
- Xiang Wang; Jianping Zhang; Yonghong Ya, Discrimination Between Pathological and Normal Voices Using GMM-SVM Approach, Journal of Voice, Vol. 25, No. 1, pp. 38-43, 2011. https://doi.org/10.1016/j.jvoice.2009.08.002
- R. Das, A comparison of multiple classification methods for diagnosis of Parkinson disease, Expert Systems with Applications, Vol. 37, No.2, pp.1568-1572, 2010. https://doi.org/10.1016/j.eswa.2009.06.040