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

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

Received:
January 25, 2023
Accepted:
May 8, 2023
Published:
September 20, 2023
Keywords:
articulation, acoustic analysis, fluctuation, propensity score, inverse probability of treatment weighting
Abstract

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:
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