Temat: Mowa - Katalog OPAC zbiorów

Skocz do pozycji: 1.

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

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Skocz do pozycji: 2.

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

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Skocz do pozycji: 3.

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

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Skocz do pozycji: 4.

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

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Skocz do pozycji: 5.

Tytuł:: The voice synthetiser of polish text for blind persons
Autorzy:: Porwik, P.
Szczepankiewicz, M.
Powiązania:: https://bibliotekanauki.pl/articles/333665.pdf
Data publikacji:: 2002
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: mowa ludzka
sztuczny generator mowy
osoba niewidoma
human speech
artificial speech generator
blind person
Opis:: In this paper we present new method of computer text analyser and computer Polish speech (words) generator. In the described computer program the grammatical characteristics of Polish speech and accents in some words have been taken into consideration. All users' actions are commented by artificial, computer voice. The group of blind students of University of Silesia have examined and tested the presented final program for over one year. Described software tool has in a lot of cases better parameters than others, commercial products.
Źródło:: Journal of Medical Informatics & Technologies; 2002, 4; MT101-109
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Speech nonfluency detection and classification based on linear prediction coefficients and neural networks
Autorzy:: Kobus, A.
Kuniszyk-Jóźkowiak, W.
Smołka, E.
Codello, I.
Powiązania:: https://bibliotekanauki.pl/articles/333600.pdf
Data publikacji:: 2010
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: przewidywanie liniowe
liniowe kodowanie predykcyjne
sieci nuronowe
kowariancja
brak płynności
mowa
wykrywanie
perceptron
linear prediction
LPC
neural networks
Kohonen
covariance
nonfluency
speech
detection
radial
Opis:: The goal of the paper is to present a speech nonfluency detection method based on linear prediction coefficients obtained by using the covariance method. The application “Dabar” was created for research. It implements three different methods of LP with the ability to send coefficients computed by them into the input of Kohonen networks. Neural networks were used to classify utterances in categories of fluent and nonfluent. The first one was Kohonen network (SOM), used to reduce LP coefficients representation of each window, which were used as input data to SOM input layer, to a vector of winning neurons of SOM output layer. Radial Basis Function (RBF) networks, linear networks and Multi-Layer Perceptrons were used as classifiers. The research was based on 55 fluent samples and 54 samples with blockades on plosives (p, b, d, t, k, g). The examination was finished with the outcome of 76% classifying.
Źródło:: Journal of Medical Informatics & Technologies; 2010, 15; 135-143
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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