Detection of fillers in the speech by people who stutter

• Autorzy: Suszyński Waldemar , Charytanowicz Małgorzata , Rosa Wojciech , Koczan Leopold , Stęgierski Rafał

Identyfikatory

DOI

10.23743/acs-2021-28

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

stuttering; fillers disfluency; automatic recognition; fillers detection

PL

jąkanie; dysfluencja; automatyczne rozpoznawanie; wykrywanie

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2021
• Tom: Vol. 17, no 4
• Strony: 45--54
• Opis fizyczny: Bibliogr. 19 poz., fig.

Twórcy

autor

Suszyński Waldemar

• Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Poland

• https://orcid.org/%5B0000-0003-2990-2078

autor

Charytanowicz Małgorzata

• Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, Poland

• https://orcid.org/0000-0002-1956-3941

autor

Rosa Wojciech

• Lublin University of Technology, Faculty of Technology Fundamentals

• https://orcid.org/0000-0002-7051-6008

autor

Koczan Leopold

• Lublin University of Technology, Faculty of Technology Fundamentals

• https://orcid.org/0000-0002-7775-1836

autor

Stęgierski Rafał

• Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science

• https://orcid.org/0000-0001-7225-3275

Bibliografia

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• [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
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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).