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    Wyświetlanie 1-7 z 7
    Tytuł:
    Improved approach to automatic detection of speech disorders based on the hidden Markov models approach
    Autorzy:
    Wiśniewski, M.
    Kuniszyk-Jóźkowiak, W.
    Smołka, E.
    Suszyński, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/333602.pdf
    Data publikacji:
    2010
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznanie
    mowa
    zaburzenia
    HMM
    recognition
    speech
    disorders
    Opis:
    In the work algorithms commonly utilized in continuous speech recognition systems were applied to detection of speech disorders. The used algorithms were briefly described and the final method of speech disorders detection was presented. The article includes the results of the short test performed in order to check the effectiveness and accuracy of the method. The aim of the test was detection and classification of fricative phonemes prolongation one of the most common speech disorders in the Polish language. It is worth emphasizing that this method enables detection of a category of speech disturbance (e.g. fricative, nasal, vowels, etc… prolongation), but also provides the information about a specific phoneme being disturbed.
    Źródło:
    Journal of Medical Informatics & Technologies; 2010, 15; 145-152
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic detection of stuttering in a speech
    Autorzy:
    Wiśniewski, M.
    Kuniszyk-Jóźkowiak, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/334003.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    speech
    disorders
    recognition
    HMM
    mowa
    zaburzenia
    rozpoznawanie
    Opis:
    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.
    Źródło:
    Journal of Medical Informatics & Technologies; 2015, 24; 31-37
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic detection and classification of phoneme repetitions using HTK toolkit
    Autorzy:
    Wiśniewski, M.
    Kuniszyk-Jóźkowiak, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/333371.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznanie
    mowa
    zaburzenia
    HMM
    recognition
    speech
    disorders
    Opis:
    The therapy of stuttering people is based on a proper selection of texts and then on a practice of their articulation by reading or narration. The texts are chosen on the basis of kind and intensity of dysfluencies appearing in a speech. Thus there is still a requirement to find effective and objective methods of analysis of dysfluent speech. Hidden Markov models are stochastic models widely used in recognition of any patterns appearing in a signal. In the work a simple monophone system based on the Hidden Markov Model Toolkit was built and tested in the context of detection and classification of phoneme repetitions - a common speech disorder in the Polish language.
    Źródło:
    Journal of Medical Informatics & Technologies; 2011, 17; 141-147
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic detection of prolonged fricative phonemes with the Hidden Markov Models approach
    Autorzy:
    Wiśniewski, M.
    Kuniszyk-Jóźkowiak, W.
    Smołka, E.
    Suszyński, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/333954.pdf
    Data publikacji:
    2007
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    ukryty model Markowa
    rozpoznawanie
    mowa
    zaburzenia
    HMM
    recognition
    speech
    disorders
    Opis:
    The Hidden Markov Model (HMM) is a stochastic approach to recognition of patterns appearing in an input signal. In the work author's implementation of the HMM were used to recognize speech disorders - prolonged fricative phonemes. To achieve the best recognition effectiveness and simultaneously preserve reasonable time required for calculations two problems need to be addressed: the choice of the HMM and the proper preparation of an input data. Tests results for recognition of the considered type of speech disorders are presented for HMM models with different number of states and for different sizes of codebooks.
    Źródło:
    Journal of Medical Informatics & Technologies; 2007, 11; 293-297
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic disordered sound repetition recognition in continuous speech using CWT and kohonen network
    Autorzy:
    Codello, I.
    Kuniszyk-Jóźkowiak, W.
    Smołka, E.
    Kobus, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/106192.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    speech recognition
    speech disorders
    sound repetition
    Continuous Wavelet Transform
    WaveBlaster
    Opis:
    Automatic disorders recognition in speech can be very helpful for a therapist while monitoring therapy progress of patients with disordered speech. This article is focused on sound repetitions. The signal is analyzed using Continuous Wavelet Transform with 16 bark scales. Using the silence finding algorithm, only speech fragments are automatically found and cut. Each cut fragment is converted into a fixed-length vector and passed into the Kohonen network. Finally, the Kohonen winning neuron result is put on the 3-layer perceptron. Most of the analysis was performer and the results were obtained using the authors’ program WaveBlaster. We use the STATISTICA package for finding the best perceptron which was then imported back into WaveBlaster and used for automatic blockades finding. The problem presented in this article is a part of our research work aimed at creating an automatic disordered speech recognition system.
    Źródło:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2012, 12, 2; 39-48
    1732-1360
    2083-3628
    Pojawia się w:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic prolongation recognition in disordered speech using CWT and Kohonen network
    Autorzy:
    Codello, I.
    Kuniszyk-Jóźkowiak, W.
    Smołka, E.
    Kobus, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/332965.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    sieć Kohonena
    zaburzenia automatycznego rozpoznawania mowy
    ciągła transformata falkowa
    skala Barka
    wydłużenie mowy
    Kohonen network
    automatic disorders speech recognition
    waveblaster
    CWT
    continuous wavelet transform (CWT)
    Bark scale
    speech prolongations
    Opis:
    Automatic disorder recognition in speech can be very helpful for the therapist while monitoring therapy progress of the patients with disordered speech. In this article we focus on prolongations. We analyze the signal using Continuous Wavelet Transform with 18 bark scales, we divide the result into vectors (using windowing) and then we pass such vectors into Kohonen network. Quite large search analysis was performed (5 variables were checked) during which, recognition above 90% was achieved. All the analysis was performed and the results were obtained using the authors' program - "WaveBlaster". It is very important that the recognition ratio above 90% was obtained by a fully automatic algorithm (without a teacher) from the continuous speech. The presented problem is part of our research aimed at creating an automatic prolongation recognition system.
    Źródło:
    Journal of Medical Informatics & Technologies; 2012, 20; 137-144
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Disordered sound repetition recognition in continuous speech using CWT and Kohonen network
    Autorzy:
    Codello, I.
    Kuniszyk-Jóźkowiak, W.
    Smołka, E.
    Kobus, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/333359.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    sieć Kohonena
    zaburzenia automatycznego rozpoznawania mowy
    ciągła transformata falkowa
    skala Barka
    powtarzanie dźwięku
    Kohonen network
    automatic disorders speech recognition
    waveblaster
    CWT
    continuous wavelet transform (CWT)
    Bark scale
    sound repetition
    Opis:
    Automatic disorders recognition in speech can be very helpful for therapist while monitoring therapy progress of patients with disordered speech. This article is focused on sound repetitions. The signal is analyzed using Continuous Wavelet Transform with 16 bark scales, the result is divided into vectors and passed into Kohonen network. Finally, the Kohonen winning neuron result is put on the 3-layer perceptron. The recognition ratio was increased by about 20% by adding a modification into the Kohonen network training process as well as into CWT computation algorithm. All the analysis was performed and the results were obtained using the authors' program ”WaveBlaster“, The problem presented in this article is a part of our research work aimed at creating an automatic disordered speech recognition system.
    Źródło:
    Journal of Medical Informatics & Technologies; 2011, 17; 123-130
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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