Automatic detection of stuttering in a speech

• Autorzy: Wiśniewski M. , Kuniszyk-Jóźkowiak W.
• Języki publikacji: EN

Abstrakty

EN

In the work authors applied speech recognition techniques to find disfluent events. The recognition system based on the Hidden Markov Model Toolkit was built and tested. The set of context dependent HMM models was trained and used to locate speech disturbances. Authors were not concentrated on specific disfluency type but tried to find any extraneous sounds in a speech signal. Patients read prepared sentences, the system recognized them and then results were compared to manual transcriptions. It allowed the system to be more robust and enabled to find all disfluencies types appearing at word boundaries. Such system can by utilized in many ways, for example like a "preprocessor" that finds strange sounds in a speech to be analyzed or classified by other algorithms later, to evaluate or track therapy process of stuttering people, to evaluate speech fluency by ´normal´ speakers, etc.

Słowa kluczowe

EN

speech; disorders; recognition; HMM

PL

mowa; zaburzenia; rozpoznawanie; HMM

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2015
• Tom: Vol. 24
• Strony: 31--37
• Opis fizyczny: Bibliogr. 12 poz., tab.

Twórcy

autor

Wiśniewski M.

• Institute of Computer Science, Maria Curie-Skłodowska University, ul. Akademicka 9, 20-031 Lublin, Poland

autor

Kuniszyk-Jóźkowiak W.

• Józef Piłsudski University of Physical Education in Warsaw, Faculty of Physical Education and Sport in Biała Podlaska, ul. Akademicka 2, 21-500 Biała Podlaska, Poland

Bibliografia

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• [12] WIŚNIEWSKI M., KUNISZYK-JÓŹKOWIKAK W., Automatic detection and classification of phoneme repetitions using HTK toolkit, Journal of Medical Informatics & Technologies, Vol. 17/2011, Computer Systems Dep., University of Silesia, Poland, 2011, pp. 143-148.