Comparisons of voice quality parameter values measured with MDVP, Praat, and TF32

MDVP, Praat, TF32에 따른 음향학적 측정치에 대한 비교

  • Ko, Hye-Ju (Department of Psychological Rehabilitation, Myongji University) ;
  • Woo, Mee-Ryung (Rehabilitation Medical Center, National Health Insurance Corporation Ilsan Hospital) ;
  • Choi, Yaelin (Department of Psychological Rehabilitation & Speech-Language Pathology, Myongji University)
  • 고혜주 (명지대학교 심리재활학학과간협동과정) ;
  • 우미령 (국민건강보험 일산병원 재활치료센터) ;
  • 최예린 (명지대학교 심리재활학학과간협동과정 & 언어치료학과)
  • Received : 2020.06.05
  • Accepted : 2020.09.14
  • Published : 2020.09.30


Measured values may differ between Multi-Dimensional Voice Program (MDVP), Praat, and Time-Frequency Analysis software (TF32), all of which are widely used in voice quality analysis, due to differences in the algorithms used in each analyzer. Therefore, this study aimed to compare the values of parameters of normal voice measured with each analyzer. After tokens of the vowel sound /a/ were collected from 35 normal adult subjects (19 male and 16 female), they were analyzed with MDVP, Praat, and TF32. The mean values obtained from Praat for jitter variables (J local, J abs, J rap, and J ppq), shimmer variables (S local, S dB, and S apq), and noise-to-harmonics ratio (NHR) were significantly lower than those from MDVP in both males and females (p<.01). The mean values of J local, J abs, and S local were significantly lower in the order MDVP, Praat, and TF32 in both genders. In conclusion, the measured values differed across voice analyzers due to the differences in the algorithms each analyzer uses. Therefore, it is important for clinicians to analyze pathologic voice after understanding the normal criteria used by each analyzer when they use a voice analyzer in clinical practice.

음질 분석에 매우 유용한 Multi-Dimensional Voice Program (MDVP), Praat, Time-Frequency Analysis software (TF32)는 각각의 음향학적 검사에 사용된 알고리즘 차이로 인해 그 측정치에 차이가 있을 수 있다. 그러므로 본 연구에서는 각각의 음향학적 검사 도구로 음성 측정치를 비교 분석하여 분석 도구에 따른 음향학적 검사 변수의 차이를 살펴보고자 하였다. 정상 성인 총 35명 (남성 19명, 여성 16명)을 대상으로 모음 /아/를 수집한 후, 동일한 음성을 MDVP, Praat, TF32 각각의 음향학적 검사 도구로 분석하였다. 그 결과 jitter 변수(J local, J abs, J rap, J ppq), shimmer 변수(S local, S dB, S apq), noise-to-harmonics ratio (NHR) 평균의 경우, 남성과 여성 모두 MDVP보다 Praat의 수치가 통계적으로 유의하게 낮았다(p<.01). 또한 J local, J abs, S local 평균의 경우, 남성과 여성 모두 MDVP, Praat, TF32 순으로 통계적으로 유의하게 낮아졌다. 결론적으로 각 음향학적 검사 도구에 사용된 알고리즘 차이로 인해 도구 간의 측정치에 차이가 있었다. 그러므로 임상가들이 임상현장에서 각각의 음향학적 검사 도구를 사용할 때 각 도구의 알고리즘에 대해 이해한 후 병적 음성을 분석하는 것이 중요할 것이다.



  1. Ahn, C. S., & Oh, S. Y. (2012). CHMM modeling using LMS algorithm for continuous speech recognition improvement. Journal of Digital Convergence, 10(11), 377-382.
  2. Amir, O., Wolf, M., & Amir, N. (2009). A clinical comparison between two acoustic analysis softwares: MDVP and Praat. Biomedical Signal Processing and Control, 4(3), 202-205.
  3. Baek, S. E., Kim, J. Y., Na, S. Y., & Choi, S. H. (2005). Speaker separation based on directional filter and harmonic filter. Phonetics and Speech Sciences,12(3), 125-136.
  4. Baek, Y., Kim, S., Kim, E., & Choi, Y. (2012). Vocal acoustic characteristics of speakers with depression. Phonetics and Speech Sciences, 4(1), 91-98.
  5. Bielamowicz, S., Kreiman, J., Gerratt, B. R., Dauer, M. S., & Berke, G. S. (1996). Comparison of voice analysis systems for perturbation measurement. Journal of Speech, Language, and Hearing Research, 39(1), 126-134.
  6. Boersma, P. (2009). Should jitter be measured by peak picking or by waveform matching? Folia Phoniatrica et Logopaedica, 61(5), 305-308.
  7. Burris, C., Vorperian, H. K., Fourakis, M., Kent, R. D., & Bolt, D. M. (2014). Quantitative and descriptive comparison of four acoustic analysis systems: Vowel measurements. Journal of Speech, Language, and Hearing Research, 57(1), 26-45.
  8. Chae, S. W., Choi, G., Kang, H. J., Choi, J. O., & Jin, S. M. (2001). Clinical analysis of voice change as a parameter of premenstrual syndrome. Journal of Voice, 15(2), 278-283.
  9. Choi, S. H., Nam, D. H., Lee, S. H., Jung, W. H., Kim, D. W., & Choi, H. S. (2005). Jitter and shimmer measurements of dysphonia among the different voice analysis programs. Journal of The Korean Society of Laryngology, Phoniatrics and Logopedics, 16(2), 140-145.
  10. Deliyski, D. D., Shaw, H. S., & Evans, M. K. (2005). Influence of sampling rate on accuracy and reliability of acoustic voice analysis. Logopedics Phoniatrics Vocology, 30(2), 55-62.
  11. Deliyski, D. D., Shaw, H. S., Evans, M. K., & Vesselinov, R. (2006). Regression tree approach to studying factors influencing acoustic voice analysis. Folia Phoniatrica et Logopaedica, 58(4), 274-288.
  12. Hirano, M. (1981). “GRBAS” scale for evaluating the hoarse voice & frequency range of phonation. Clinical Examination of Voice, 5, 83-84.
  13. KayPENTAX(2005). Multi-Dimensional Voice Program(MDVP) Model 5105. Instruction Manual. A Division of PENTAX Medical Company 2 Bridgewater Lane Lincoln Park, NJ.
  14. Ko, D. H. (2003). A study of extracting acoustic parameters for individual speakers. Phonetics and Speech Sciences, 10(2), 129-143.
  15. Ko, H. J., Kang, M. J., Kwon, H. J., Choi, Y., Lee, M. G., & Choi, H. S. (2013). Acoustic characteristics on the adolescent period aged from 16 to 18 years. Phonetics and Speech Sciences, 5(1), 81-90.
  16. Lindholm, P., Vilkman, E., Raudaskoski, T., Suvanto-Luukkonen, E., & Kauppila, A. (1997). The effect of postmenopause and postmenopausal HRT on measured voice values and vocal symptoms. Maturitas, 28(1), 47-53.
  17. Linville, S. E. (1996). The sound of senescence. Journal of voice, 10(2), 190-200.
  18. Maryn, Y., Corthals, P., De Bodt, M., Van Cauwenberge, P., & Deliyski, D. (2009). Perturbation measures of voice: A comparative study between multi-dimensional voice program and praat. Folia Phoniatrica et Logopaedica, 61(4), 217-226.
  19. Milenkovic, P. (1987). Least mean square measures of voice perturbation. Journal of Speech, Language, and Hearing Research, 30(4), 529-538.
  20. Nam, K. C., Lee, S. H., Choi, J. N., Choi, H. S., Nam, D. H., & Kim, D. W. (2005, May). Comparison of vowel pitch results among several commercial voice analysis programs. Proceedings of the KIEE Conference (pp. 54-56). Seoul, Korea.
  21. Natour, Y. S., & Saleem, A. F. (2009). The performance of the time-frequency analysis software (TF32) in the acoustic analysis of the synthesized pathological voice. Journal of Voice, 23(4), 414-424.
  22. Oguz, H., Kiliç, M. A., & ŞAFAK, M. A. (2011). Comparison of results in two acoustic analysis programs: Praat and MDVP. Turkish Journal of Medical Sciences, 41(5), 835-841.
  23. Park, S. B. (2005). A voice signal transformation scheme for voice-based music retrieval (Master's thesis). Ajou University, Korea.
  24. Paul, B., & David, W. (2018) . Praat manual (version 6.0.42). Amsterdam, the Netherlands: University of Amsterdam.
  25. Paul, H. M. (2018). TF32 User's manual. Madison, WI: University of Wisconsin-Madison. Retrieved from
  26. Pyo, H. Y., Sim, H. S., & Lim, S. E. (2000). The change of correlation between GRBAS scales and MDVP parameters according to the different length of voice samples for MDVP analysis. Phonetics and Speech Sciences, 7(2), 71-81.
  27. Pyo, H. Y., & Sim, H. S. (2007). A study for the development of Korean voice assessment model for the patients with voice disorders: A qualitative study. Phonetics and Speech Sciences, 14(2), 7-22.
  28. Pyo, H. Y., Sim, H. S., Song, Y. K., Yoon, Y. S., Lee, E. K., Lim, S. E., Hah, H. R., & Choi, H. S. (2002). The acoustic study on the voices of Korean normal adults. Phonetics and Speech Sciences, 9(2), 179-192.
  29. Pyo, H. Y., & Song, Y. (2010). Recent trends in evaluation and diagnosis of voice disorders: A literature review. Communication Sciences and Disorders, 15(4), 506-525.
  30. Rabiner, L. R., & Schafer, R. W. (1978). Digital processing of speech signals. Englewood Cliffs, NJ: Prentice Hall.
  31. Shim, S. Y., Kim, H. H., Kim, J. O., & Shin, J. C. (2014). Difference in voice parameters of MDVP and praat programs according to severity of voice disorders in vocal nodule. Phonetics and Speech Sciences, 6(2), 107-114.
  32. Shin, D. S., Kim, J., & Bae, M. J. (2000). On a pitch detection using AME in Transition Region (Rearch report). Jincheon, Korea: National IT Industry Agency.
  33. Styler, W. (2017). Using Praat for linguistic research. Version 1.8.1. Retrieved from on August 1, 2018.
  34. Syntrillium. (n.d.). Cool Edit Pro [Computer Software].
  35. TF32 and CSpeech. (2005). Retrieved from
  36. Van Lieshout, P. (2017). Praat short tutorial (version 5.0). Toronto, ON: University of Toronto. Retrieved from https://www.
  37. Yoo, J. Y., Jeong, O. R., Jang, T. Y., & Ko, D. H. (2003). A Correlation study among acoustic parameters of MDVP, Praat, and Dr. Speech. Phonetics and Speech Sciences, 10(3), 29-36.
  38. Yun, S. Y., & Kwon, D. H. (1998). Acoustic characteristics of normal children's voice of 5 to 11 years old. Journal of Speech-Language & Hearing Disorders, 7(1), 67-78.