JACIII Vol.27 No.5 pp. 848-854
doi: 10.20965/jaciii.2023.p0848

Research Paper:

Abnormal Articulation Detecting Model with Fluctuation Measurements Using Acoustic Analysis

Naomi Yagi*,† ORCID Icon, Yutaka Hata** ORCID Icon, and Yoshitada Sakai*** ORCID Icon

*Advanced Medical Engineering Research Institute, University of Hyogo
3-264 Kamiya, Himeji, Hyogo 670-0836, Japan

Corresponding author

**Graduate School of Information Science, University of Hyogo
7-1-28 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan

***Division of Rehabilitation Medicine, Graduate School of Medicine, Kobe University
7-5-1 Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan

January 25, 2023
May 8, 2023
September 20, 2023
articulation, acoustic analysis, fluctuation, propensity score, inverse probability of treatment weighting

Articulation disorder is a condition in which the mouth, tongue, vocal cords, and other parts of the body that play an important role in producing voice are damaged, resulting in the inability to produce sound. To diagnose articulation disorders, the movement and shape of each organ concerned with pronunciation are examined. If necessary, the underlying disease or disorder should be managed properly. In it, a speech therapist tests your pronunciation. The observation of conversation and the examination of the pronunciation of each syllable are used to distinguish between mistakes and the degree of articulation disorder. However, these processes are time consuming and labor intensive and are subjective judgments by experts. Therefore, it is important to investigate the characteristics of vocal signals by acoustic analysis of speech objectively. In this study, we focused on fluctuations in the period and amplitude of speech signals and predicted a model for detecting abnormal articulations using fluctuation measurement of the voice data in six healthy subjects and nine patients with an articulation disorder. We used inverse probability of treatment weighting to match the variability for the two groups using the inverse of propensity scores. As the results, the classification performance area under the curve was 0.781 (sensitivity: 0.781, specificity: 0.680) for healthy subjects and patients. We conclude that acoustic analyzing techniques are useful for diagnosing and treating articulation disorders.

Cite this article as:
N. Yagi, Y. Hata, and Y. Sakai, “Abnormal Articulation Detecting Model with Fluctuation Measurements Using Acoustic Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.5, pp. 848-854, 2023.
Data files:
  1. [1] J. R. Duffy, “Motor Speech Disorders: Substrates, Differential Diagnosis, and Management,” Mosby, 1995.
  2. [2] K. M. Yorkston, P. A. Dowden, and D. R. Beukelman, “Intelligibility measurement as a tool in the clinical management of dysarthric speakers,” R. D. Kent (Ed.), “Intelligibility in Speech Disorders: Theory, Measurement, and Management,” pp. 265-285, John Benjamins Publishing Company, 1992.
  3. [3] P. Lieberman, “Some acoustic measures of the fundamental periodicity of normal and pathologic larynges,” The J. of the Acoustical Society of America, Vol.35, No.3, pp. 344-353, 1963.
  4. [4] P. Lieberman, “Some acoustic correlates of word stress in American English,” J. of the Acoustical Society of America, Vol.32, No.4, pp. 451-454, 1960.
  5. [5] M. Petrovic-Lazic, N. Jovanovic, M. Kulic, S. Babac, and V. Jurisic, “Acoustic and perceptual characteristics of the voice in patients with vocal polyps after surgery and voice therapy,” J. of Voice, Vol.29, No.2, pp. 241-246, 2015.
  6. [6] B. Barsties and M. D. de Bodt, “Assessment of voice quality: Current state-of-the-art,” Auris Nasus Larynx, Vol.42, No.3, pp. 183-188, 2015.
  7. [7] M. Hirano and K. R. McCormick, “Clinical examination of voice by Minoru Hirano,” The J. of the Acoustical Society of America, Vol.80, No.4, Article No.1273, 1986.
  8. [8] T. Bhuta, L. Patrick, and J. D. Garnett, “Perceptual evaluation of voice quality and its correlation with acoustic measurements,” J. of Voice, Vol.18, No.3, pp. 299-304, 2004.
  9. [9] P. Verma, M. Pal, and A. Raj, “Objective acoustic analysis of voice improvement after phonosurgery,” Indian J. of Otolaryngology and Head & Neck Surgery, Vol.62, pp. 131-137, 2010.
  10. [10] N. Yagi, Y. Oku, S. Nagami, Y. Yamagata, J. Kayashita, A. Ishikawa, K. Domen, and R. Takahashi, “Inappropriate timing of swallow in the respiratory cycle causes breathing-swallowing discoordination,” Frontiers in Physiology, Vol.8, Article No.676, 2017.
  11. [11] Y. D. Heman-Ackah, D. D. Michael, and G. S. Goding Jr., “The relationship between cepstral peak prominence and selected parameters of dysphonia,” J. of Voice, Vol.16, No.1, pp. 20-27, 2002.
  12. [12] Y. Maryn, N. Roy, M. de Bodt, P. van Cauwenberge, and P. Corthals, “Acoustic measurement of overall voice quality: A meta-analysis,” The J. of the Acoustical Society of America, Vol.126, No.5, pp. 2619-2634, 2009.
  13. [13] N. Yagi, S. Nagami, M.-K. Lin, T. Yabe, M. Itoda, T. Imai, and Y. Oku, “A noninvasive swallowing measurement system using a combination of respiratory flow, swallowing sound, and laryngeal motion,” Medical and Biological Engineering and Computing, Vol.55, No.6, pp. 1001-1017, 2017.
  14. [14] E. A. Peterson, N. Roy, S. N. Awan, R. M. Merrill, R. Banks, and K. Tanner, “Toward validation of the cepstral spectral index of dysphonia (CSID) as an objective treatment outcomes measure,” J. of Voice, Vol.27, No.4, pp. 401-410, 2013.
  15. [15] N. Yagi, Y. Hata, and Y. Sakai, “Investigation of inspection methods in acoustic analysis using pronunciation feature extraction,” 2022 Int. Conf. on Machine Learning and Cybernetics (ICMLC), pp. 204-208, 2022.
  16. [16] Japanese Society of Speech and Language Medicine “Cleft Palate Dysarthria (Audio CD),” Interuna Publisher, Inc., 1999.
  17. [17] H. Kasuya, S. Ogawa, and Y. Kikuchi, “An adaptive comb filtering method as applied to acoustic analyses of pathological voice,” Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Vol.11, pp. 669-672, 1986.
  18. [18] Y. Koike, H. Takahashi, and T. C. Calcaterra, “Acoustic measures for detecting laryngeal pathology,” Acta Oto-Laryngologica, Vol.84, Nos.1-6, pp. 105-117, 1977.
  19. [19] I. R. Titze, Y. Horii, and R. C. Scherer, “Some technical considerations in voice perturbation measurements,” J. of Speech and Hearing Research, Vol.30, No.2, pp. 252-260, 1987.
  20. [20] H. Kasuya, K. Masubuchi, S. Ebihara, and H. Yoshida, “Preliminary experiments on voice screening,” J. of Phonetics, Vol.14, Nos.3-4, pp. 463-468, 1986.
  21. [21] P. H. Dejonckere, M. Remacle, E. Fresnel-Elbaz, V. Woisard, L. Crevier-Buchman, and B. Millet, “Differentiated perceptual evaluation of pathological voice quality: Reliability and correlations with acoustic measurements,” Revue de Laryngologie - Otologie - Rhinologie, Vol.117, No.3, pp. 219-224, 1996.
  22. [22] P. C. Austin and E. A. Stuart, “Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies,” Statistics in Medicine, Vol.34, No.28, pp. 3661-3679, 2015.
  23. [23] J. M. Robins, A. Rotnitzky, and L. P. Zhao, “Estimation of regression coefficients when some regressors are not always observed,” J. of the American Statistical Association, Vol.89, No.427, pp. 846-866, 1994.
  24. [24] M. A. Hernán and J. M. Robins, “Estimating causal effects from epidemiological data,” J. of Epidemiology and Community Health, Vol.60, No.7, pp. 578-586, 2006.
  25. [25] P. C. Austin, “The performance of different propensity score methods for estimating marginal hazard ratios,” Statistics in Medicine, Vol.32, No.16, pp. 2837-2849, 2013.
  26. [26] J. H. McDonald, “Multiple Logistic Regression,” Handbook of Biological Statistics, 2009.
  27. [27] D. W. Hosmer Jr., S. Lemeshow, and R. X. Sturdivant, “Applied Logistic Regression,” John Wiley & Sons, 2013.
  28. [28] M. A. Efroymson, “Multiple regression analysis,” A. Ralston and H. S. Wilf (Eds.), “Mathematical Methods for Digital Computers,” pp. 191-203, John Wiley, 1960.
  29. [29] D. W. Hosmer and S. Lemesbow, “Goodness of fit tests for the multiple logistic regression model,” Communications in Statistics – Theory and Methods, Vol.9, No.10, pp. 1043-1069, 1980.
  30. [30] W. G. William, “The χ2 test of goodness of fit,” Annals of Mathematical Statistics, Vol.23, No.3, pp. 315-345, 1952.
  31. [31] R. R. Hocking, “A Biometrics Invited Paper – The analysis and selection of variables in linear regression,” Biometrics, Vol.32, No.1, pp. 1-49, 1976.
  32. [32] M. H. Zweig and G. Campbell, “Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine,” Clinical Chemistry, Vol.39, No.4, pp. 561-577, 1993.
  33. [33] A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,” Pattern Recognition, Vol.30, No.7, pp. 1145-1159, 1997.
  34. [34] N. Yagi, Y. Sakai, N. Kawamura, H. Maezawa, Y. Hata, M. Hirata, H. Kashioka, and T. Yanagida, “Singing experience influences RSST scores,” Healthcare, Vol.10, No.2, Article No.377, 2022.
  35. [35] P. C. Austin, “An introduction to propensity score methods for reducing the effects of confounding in observational studies,” Multivariate Behavioral Research, Vol.46, No.3, pp. 399-424, 2011.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024