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Abstrakty
Stuttering is a speech impediment that is a very complex disorder. It is difficult to diagnose and treat, and is of unknown initiation, despite the large number of studies in this field. Stuttering can take many forms and varies from person to person, and it can change under the influence of external factors. Diagnosing and treating speech disorders such as stuttering requires from a speech therapist, not only good professional prepa-ration, but also experience gained through research and practice in the field. The use of acoustic methods in combination with elements of artificial intelligence makes it possible to objectively assess the disorder, as well as to control the effects of treatment. The main aim of the study was to present an algorithm for automatic recognition of fillers disfluency in the statements of people who stutter. This is done on the basis of their parameterized features in the amplitude-frequency space. The work provides as well, exemplary results demonstrating their possibility and effectiveness. In order to verify and optimize the procedures, the statements of seven stutterers with duration of 2 to 4 minutes were selected. Over 70% efficiency and predictability of automatic detection of these disfluencies was achieved. The use of an automatic method in conjunction with therapy for a stuttering person can give us the opportunity to objectively assess the disorder, as well as to evaluate the progress of therapy.
Czasopismo
Rocznik
Tom
Strony
45--54
Opis fizyczny
Bibliogr. 19 poz., fig.
Twórcy
autor
- Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Poland
autor
- Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Poland
autor
- Lublin University of Technology, Faculty of Technology Fundamentals
autor
- Lublin University of Technology, Faculty of Technology Fundamentals
autor
- Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science
Bibliografia
- [1] Alharbia, S., Hasana, M., Simonsa, A. J. H., Brumfitt, S., & Green, P. (2020). Sequence labeling to detect stuttering events in read speech. Computer Speech & Language, 62, 101052. http://doi.org/10.1016/j.csl.2019.101052
- [2] Bloodstein, O. (1995). A handbook on stuttering. Singular Publishing Group, Inc.
- [3] Czyżewski, A., Kaczmarek, A., & Kostek, B. (2003). Intelligent processing of stuttered speech. Journal of Intelligent Inform. Systems, 143–171.
- [4] Howell, P., & Sackin, S. J. (1995). Automatic recognition of repetitions and prolongations in stuttered speech, Stuttering. Proceedings of the First World Congress on Fluency Disorders (pp. 372–374). Munich.
- [5] Howell, P., Sackin, S. J., Glenn, K., & Au-Yeung, J. (1997). Automatic stuttering frequency counts, Speech Motor Production and Fluency Disorders. Elsevier.
- [6] Kuniszyk-Jóźkowiak, W., Dzieńkowski, M., Smołka E., & Suszyński, W. (2003). Computer Diagnosis and Therapy of Stuttering. Structures – Waves – Human Health, VIII(2), 133–144.
- [7] Kuniszyk-Jóźkowiak, W., Smołka, E., & Suszyński, W. (2001). Acoustical characteristics alteration in persons who stutter resulting from therapy. Structures-Waves-Biomedical Engineering, X(2), 57–68.
- [8] Kuniszyk-Jóźkowiak, W., Smołka, E., Dzieńkowski, M., & Suszyński W. (2004). Computer therapy of speech non-fluency with automatic adaptation of individual person's difficulties. Structures-Waves-Human Health, VIII(2), 63–70.
- [9] Moore, B. C. J., & Glasberg, B. R. (1983). Suggested formulae for calculating auditory-filter banwidths and excitation patterns. The Journal of the Acoustical Society of America, 74, 750–753.
- [10] Moore, B. C. J., Peters, R. W., & Glasberg, B. R. (1990). Auditory filters shapes at low center frequencies. The Journal of the Acoustical Society of America, 88, 132–149.
- [11] Smołka, E., Kuniszyk-Jóźkowiak, W., Suszyński, W., & Dzieńkowski, M. (2003). Speech syllabic structure extraction with application of Kohonen network. Annales Informatica Universitatis Mariae Curie-Skłodowska, AI 1,125–131.
- [12] Stromsta, C. (1993). The nature and management of stuttering. Proceedings Abstracta, Congressus XVIII (pp. 16–18). Societatis Phoniatricae Europaeae, Praga.
- [13] Suszyński, W., Kuniszyk-Józkowiak, W., Smolka, E., & Dzienkowski, M. (2003). Automatic Recognition of Nasals Prolongations in the Speech of Persons who Stutter. Structures-Waves-Human Health, XII(2), 175–184.
- [14] Suszyński, W., Kuniszyk-Jóźkowiak, W., Smołka, E., & Dzieńkowski, M. (2003). Prolongation detection with application of fuzzy logic. Annales Informatica Universitatis Mariae Curie-Skłodowska, AI 1, 133–140.
- [15] Suszyński, W., Kuniszyk-Jóźkowiak, W., Smołka, E., & Dzieńkowski, M. (2005). Speech disfluency detection with correlative method. Annales Informatica Universitatis Mariae Curie-Skłodowska, AI 3, 131–138.
- [16] Świetlicka, I., Kuniszyk-Jóźkowiak, W., & Smołka, E. (2013). Hierarchical ANN system for stuttering identification. Computer Speech & Language, 27(1), 228–242. https://doi.org/10.1016/j.csl.2012.05.003
- [17] Wingate, M. E. (2002). Foundation of stuttering. Academic Press.
- [18] Wiśniewski, M, Kuniszyk-Jóźkowiak, W., Smołka, E., & Suszyński, W. (2010). Improved Approach to Automatic Detection of Speech Disorders Based the Hidden Markov Models Approach. Journal of Medical Informatics & Technologies, 15, 145–152. http://doi.org/10.1007/978-3-540-75175-5_56
- [19] Wiśniewski, M., & Kuniszyk-Jóźkowiak, W. (2015). Automatic detection of stuttering in a speech. Journal of Medical Informatics & Technologies, 24, 31–37.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6de8534e-57fa-43c6-841a-a387779d6bba