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.

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. https://doi.org/10.1121/1.1918465
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. https://doi.org/10.1121/1.1908095
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. https://doi.org/10.1016/j.jvoice.2014.07.009
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. https://doi.org/10.1016/j.anl.2014.11.001
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. https://doi.org/10.1121/1.393788
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. https://doi.org/10.1016/j.jvoice.2003.12.004
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. https://doi.org/10.1007/s12070-010-0024-6
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. https://doi.org/10.3389/fphys.2017.00676
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. https://doi.org/10.1016/s0892-1997(02)00067-x
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. https://doi.org/10.1121/1.3224706
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. https://doi.org/10.1007/s11517-016-1561-2
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. https://doi.org/10.1016/j.jvoice.2013.04.002
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. https://doi.org/10.1109/ICMLC56445.2022.9941300
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. https://doi.org/10.1109/ICASSP.1986.1168996
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. https://doi.org/10.3109/00016487709123948
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. https://doi.org/10.1044/jshr.3002.252
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. https://doi.org/10.1016/s0095-4470(19)30690-4
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. https://doi.org/10.1002/sim.6607
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. https://doi.org/10.2307/2290910
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. https://doi.org/10.1136/jech.2004.029496
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. https://doi.org/10.1002/sim.5705
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. https://doi.org/10.1002/9781118548387
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. https://doi.org/10.1214/aoms/1177729380
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. https://doi.org/10.2307/2529336
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. https://doi.org/10.1093/clinchem/39.4.561
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. https://doi.org/10.1016/S0031-3203(96)00142-2
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. https://doi.org/10.3390/healthcare10020377
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. https://doi.org/10.1080/00273171.2011.568786