DETECTION OF FILLERS IN THE SPEECH BY PEOPLE WHO STUTTER
Waldemar SUSZYŃSKI
w.suszynski@pollub.pl* Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science (Poland)
Małgorzata CHARYTANOWICZ
Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science (Poland)
Wojciech ROSA
Lublin University of Technology, Faculty of Technology Fundamentals (Poland)
Leopold KOCZAN
Lublin University of Technology, Faculty of Technology Fundamentals (Poland)
Rafał STĘGIERSKI
Lublin University of Technology, Faculty of Electrical Engineering and Computer Science (Poland)
Abstract
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 profes-sional preparation, 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.
Keywords:
stuttering, fillers disfluency, automatic recognition, fillers detectionReferences
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
DOI: https://doi.org/10.1016/j.csl.2019.101052
Google Scholar
Bloodstein, O. (1995). A handbook on stuttering. Singular Publishing Group, Inc.
Google Scholar
Czyżewski, A., Kaczmarek, A., & Kostek, B. (2003). Intelligent processing of stuttered speech. Journal of Intelligent Inform. Systems, 143–171.
DOI: https://doi.org/10.1023/A:1024710532716
Google Scholar
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.
Google Scholar
Howell, P., Sackin, S. J., Glenn, K., & Au-Yeung, J. (1997). Automatic stuttering frequency counts, Speech Motor Production and Fluency Disorders. Elsevier.
Google Scholar
Kuniszyk-Jóźkowiak, W., Dzieńkowski, M., Smołka E., & Suszyński, W. (2003). Computer Diagnosis and Therapy of Stuttering. Structures – Waves – Human Health, VIII(2), 133–144.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
DOI: https://doi.org/10.1121/1.389861
Google Scholar
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.
DOI: https://doi.org/10.1121/1.399960
Google Scholar
Smołka, E., Kuniszyk-Jóźkowiak, W., Suszyński, W., & Dzieńkowski, M. (2003). Speech syllabic structure extraction with application of Kohonen network. Annales Informatica Universitatis Mariae CurieSkłodowska, AI 1,125–131.
Google Scholar
Stromsta, C. (1993). The nature and management of stuttering. Proceedings Abstracta, Congressus XVIII (pp. 16–18). Societatis Phoniatricae Europaeae, Praga.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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
DOI: https://doi.org/10.1016/j.csl.2012.05.003
Google Scholar
Wingate, M. E. (2002). Foundation of stuttering. Academic Press.
DOI: https://doi.org/10.1163/9789004487192
Google Scholar
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
DOI: https://doi.org/10.1007/978-3-540-75175-5_56
Google Scholar
Wiśniewski, M., & Kuniszyk-Jóźkowiak, W. (2015). Automatic detection of stuttering in a speech. Journal of Medical Informatics & Technologies, 24, 31–37.
Google Scholar
Authors
Waldemar SUSZYŃSKIw.suszynski@pollub.pl
* Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science Poland
Authors
Małgorzata CHARYTANOWICZLublin University of Technology, Faculty of Electrical Engineering and Computer Science, Department of Computer Science Poland
Authors
Wojciech ROSALublin University of Technology, Faculty of Technology Fundamentals Poland
Authors
Leopold KOCZANLublin University of Technology, Faculty of Technology Fundamentals Poland
Authors
Rafał STĘGIERSKILublin University of Technology, Faculty of Electrical Engineering and Computer Science Poland
Statistics
Abstract views: 212PDF downloads: 38
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Mohamed ELBAHRI, Nasreddine TALEB, Sid Ahmed El Mehdi ARDJOUN, Chakib Mustapha Anouar ZOUAOUI , FEW-SHOT LEARNING WITH PRE-TRAINED LAYERS INTEGRATION APPLIED TO HAND GESTURE RECOGNITION FOR DISABLED PEOPLE , Applied Computer Science: Vol. 20 No. 2 (2024)
- Shahil SHARMA, Rajnesh LAL, Bimal KUMAR, DEVELOPING MACHINE LEARNING APPLICATION FOR EARLY CARDIOVASCULAR DISEASE (CVD) RISK DETECTION IN FIJI: A DESIGN SCIENCE APPROACH , Applied Computer Science: Vol. 20 No. 3 (2024)
- Mahmoud BAKR, Sayed ABDEL-GABER, Mona NASR, Maryam HAZMAN, TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING , Applied Computer Science: Vol. 18 No. 2 (2022)
- Anna MACHROWSKA, Robert KARPIŃSKI, Marcin MACIEJEWSKI, Józef JONAK, Przemysław KRAKOWSKI, APPLICATION OF EEMD-DFA ALGORITHMS AND ANN CLASSIFICATION FOR DETECTION OF KNEE OSTEOARTHRITIS USING VIBROARTHROGRAPHY , Applied Computer Science: Vol. 20 No. 2 (2024)
- Baldemar ZURITA, Luís LUNA, José HERNÁNDEZ, Federico RAMÍREZ, BOVW FOR CLASSIFICATION IN GEOMETRICS SHAPES , Applied Computer Science: Vol. 14 No. 4 (2018)
- Elmehdi BENMALEK, Jamal EL MHAMDI, Abdelilah JILBAB, Atman JBARI, A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS , Applied Computer Science: Vol. 18 No. 4 (2022)
- Marcin TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF IMAGE ANALYSIS TO THE IDENTIFICATION OF MASS INERTIA MOMENTUM IN ELECTROMECHANICAL SYSTEM WITH CHANGEABLE BACKLASH ZONE , Applied Computer Science: Vol. 15 No. 3 (2019)
- Marcin TOMCZYK, Barbara BOROWIK, Bohdan BOROWIK, IDENTIFICATION OF THE MASS INERTIA MOMENT IN AN ELECTROMECHANICAL SYSTEM BASED ON WAVELET–NEURAL METHOD , Applied Computer Science: Vol. 14 No. 2 (2018)
- Marcin TOMCZYK, Barbara BOROWIK, Mariusz MIKULSKI, IDENTIFICATION OF A BACKLASH ZONE IN AN ELECTROMECHANICAL SYSTEM CONTAINING CHANGES OF A MASS INERTIA MOMENT BASED ON A WAVELET–NEURAL METHOD , Applied Computer Science: Vol. 14 No. 4 (2018)
- Md. Torikur RAHMAN, Mohammad ALAUDDIN, Uttam Kumar DEY, Dr. A.H.M. Saifullah SADI, ADAPTIVE SECURE AND EFFICIENT ROUTING PROTOCOL FOR ENHANCE THE PERFORMANCE OF MOBILE AD HOC NETWORK , Applied Computer Science: Vol. 19 No. 3 (2023)
You may also start an advanced similarity search for this article.
