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    Wyświetlanie 1-8 z 8
    Tytuł:
    Effect of Time-domain Windowing on Isolated Speech Recognition System Performance
    Autorzy:
    Ananthakrishna, Thalengala
    Anitha, H.
    Girisha, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/2055228.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    hidden Markov model
    HMM
    isolated speech recognition system
    ISR
    Kannada language
    mono-phone model
    Mel frequency cepstral coefficients
    MFCC
    Opis:
    Speech recognition system extract the textual data from the speech signal. The research in speech recognition domain is challenging due to the large variabilities involved with the speech signal. Variety of signal processing and machine learning techniques have been explored to achieve better recognition accuracy. Speech is highly non-stationary in nature and therefore analysis is carried out by considering short time-domain window or frame. In the speech recognition task, cepstral (Mel frequency cepstral coefficients (MFCC)) features are commonly used and are extracted for short time-frame. The effectiveness of features depend upon duration of the time-window chosen. The present study is aimed at investigation of optimal time-window duration for extraction of cepstral features in the context of speech recognition task. A speaker independent speech recognition system for the Kannada language has been considered for the analysis. In the current work, speech utterances of Kannada news corpus recorded from different speakers have been used to create speech database. The hidden Markov tool kit (HTK) has been used to implement the speech recognition system. The MFCC along with their first and second derivative coefficients are considered as feature vectors. Pronunciation dictionary required for the study has been built manually for mono-phone system. Experiments have been carried out and results have been analyzed for different time-window lengths. The overlapping Hamming window has been considered in this study. The best average word recognition accuracy of 61.58% has been obtained for a window length of 110 msec duration. This recognition accuracy is comparable with the similar work found in literature. The experiments have shown that best word recognition performance can be achieved by tuning the window length to its optimum value.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 1; 161--166
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimation, Decoding and Forecasting in HMM and Hybrid HMM/ANN Models : a Case of Seismic Events in Poland
    Autorzy:
    Bijak, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/92872.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    infrastructure subsystem
    hybrid HMM/ANN model
    neural networks
    seismic events
    Opis:
    This paper compares performance of a hidden Markov model (HMM) and a hybrid HMM/ANN model in seismic events modeling. Observation variables are assumed to follow a Poisson distribution. Parameters of the discrete-time two-state models are estimated on the basis of data on seismic events that were recorded in Poland from 1991 to 1995. Then, on the basis of the estimation results, the most likely sequences of states of the hidden Markov chains are found and forecasts for January 1996 are made. It is shown that the hybrid model fits better to the data.
    Źródło:
    Studia Informatica : systems and information technology; 2006, 1(7); 7-17
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An SFA-HMM performance evaluation method using state difference optimization for running gear systems in high-speed trains
    Autorzy:
    Cheng, Chao
    Wang, Meng
    Wang, Jiuhe
    Shao, Junjie
    Chen, Hongtian
    Powiązania:
    https://bibliotekanauki.pl/articles/2172116.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    slow feature analysis
    SFA
    performance evaluation
    hidden Markov model
    HMM
    running gear system
    analiza cech
    ocena efektywności
    ukryty model Markowa
    układ biegowy
    Opis:
    The evaluation of system performance plays an increasingly important role in the reliability analysis of cyber-physical systems. Factors of external instability affect the evaluation results in complex systems. Taking the running gear in high-speed trains as an example, its complex operating environment is the most critical factor affecting the performance evaluation design. In order to optimize the evaluation while improving accuracy, this paper develops a performance evaluation method based on slow feature analysis and a hidden Markov model (SFA-HMM). The utilization of SFA can screen out the slowest features as HMM inputs, based on which a new HMM is established for performance evaluation of running gear systems. In addition to directly classical performance evaluation for running gear systems of high-speed trains, the slow feature statistic is proposed to detect the difference in the system state through test data, and then eliminate the error evaluation of the HMM in the stable state. In addition, indicator planning and status classification of the data are performed through historical information and expert knowledge. Finally, a case study of the running gear system in high-speed trains is discussed. After comparison, the result shows that the proposed method can enhance evaluation performance.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 3; 389--402
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Diagnosis of incipient faults in nonlinear analog circuits
    Autorzy:
    Deng, Y.
    Shi, Y.
    Zhang, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/220611.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    nonlinear circuits
    fault diagnosis
    Volterra series
    fractional correlation
    hidden Markov model (HMM)
    Opis:
    Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault-free case and the incipient faulty cases of the CUT are derived. Using the feature vectors extracted from the fractional correlation functions, the hidden Markov model (HMM) is trained. Finally, the well-trained HMM is used to accomplish the incipient fault diagnosis. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.
    Źródło:
    Metrology and Measurement Systems; 2012, 19, 2; 203-218
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analytical investigation of congestion -avoidance strategies in closed-type queuing models of computer networks with priority scheduling
    Autorzy:
    Oniszczuk, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/1933179.pdf
    Data publikacji:
    2007
    Wydawca:
    Politechnika Gdańska
    Tematy:
    pre-emptive-resume queuing model
    mean value analysis (MVA)
    congestion problem
    call admission con-trol (CAC)
    hidden Markov models (HMM's)
    Opis:
    A new approach is presented to modeling intelligent admission control and congestion avoiding mechanism, without rejecting new requests, embedded into a priority closed computer network. Most Call Admission Control (CAC) algorithms treat every request uniformly and hence optimize network performance by maximizing the number of admitted and served requests. In practice, requests have various levels of importance to the network, for example priority classes. Here, the investigated closed network with priority scheduling has been reduced to two service centers, which allows for decomposition of a larger network into a chain of individual queues, where each queue can be studied in isolation. A new algorithm (approach) of this special type of closed priority queuing systems is presented, including a node consisting of several priority sources generating tasks, designated as an Infinite Server (IS), and a service centre with a single service line. This model type is frequently described as a finite source, pre-emptive-resume priority queue (with general distribution of service time). The pre-emptive service discipline allows a task of lower priority to be returned to the head of a queue when a new task of higher priority arrives. A mathematical model of provisioning and admission control mechanism is also described. The idea behind this mechanism has been derived from the Hidden Markov Model (HMM) theory. It is crucial in the CAC process that the network manager obtains correct information about the traffic characteristics declared by the user. Otherwise, the quality of service (QoS) may be dramatically reduced by accepting tasks based on erroneous traffic descriptors. Numerical results illustrate the strategy's effectiveness in avoiding congestion problems.
    Źródło:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2007, 11, 3; 237-252
    1428-6394
    Pojawia się w:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Zastosowanie metod grupowania sekwencji czasowych w rozpoznawaniu mowy na podstawie ukrytych modeli Markowa
    Application of time sequences clustering methods in the speech recognition based on hidden Markov models
    Autorzy:
    Pałys, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/273384.pdf
    Data publikacji:
    2006
    Wydawca:
    Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
    Tematy:
    HMM
    ukryte modele Markowa
    estymacja
    rozpoznawanie mowy
    hidden Markov model
    estimation
    speech recognition
    Opis:
    Artykuł dotyczy problemu tworzenia ukrytych modeli Markowa na podstawie zarejestrowanych wypowiedzi. Kluczowym problemem jest tu wyznaczenie zbioru stanów modelu Markowa. Przyjęto, że stany modelu są określone przez skupienia obserwacji. Skupienia te można uzyskać drogą grupowania sekwencji obserwacji sygnału mowy.
    A problem of hidden Markov models formation on the basis of recorded speech is considered in this paper. The key issue is the designation of a Markov model set. The assumption is that each HMM state is associated with clusters of observations. The clusters may be obtained by gathering of observations sequences for a speech signal.
    Źródło:
    Biuletyn Instytutu Automatyki i Robotyki; 2006, R. 12, nr 23, 23; 113-127
    1427-3578
    Pojawia się w:
    Biuletyn Instytutu Automatyki i Robotyki
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optymalizacja modeli HMM oraz ich zastosowanie w rozpoznawaniu mowy
    Optimalization of the models HMM and their application in speech recognition
    Autorzy:
    Szostek, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/320383.pdf
    Data publikacji:
    2005
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    proces losowy
    model Markowa
    model HMM
    optymalizacja
    rondom process
    Markov model
    optimization
    Opis:
    Modelowanie sygnału mowy za pomocą niejawnych modeli Markowa HMM (hidden Markov model) stanowi jeden z najefektywniejszych sposobów rozpoznawania mowy. Niniejszy artykuł poświęcony jest podstawom matematycznym teorii niejawnych modeli Markowa. Szczególną uwagę zwrócono w nim na wyprowadzenie zależności pozwalających stosować modele HMM do modelowania sygnałów. W pierwszej części artykułu przedstawiono wyprowadzenie zależności pozwalające dobierać parametry modelu procesu Markowa. W dalszej części artykułu przedstawione są wyprowadzenia zależności pozwalające w sposób krokowy dobierać parametry modelu łańcucha Markowa. Opisane metody oparte są na minimalizacji prawdopodobieństwa wygenerowania losowej w czasie sekwencji obserwacji w funkcji parametrów modelu. W przedstawionych w artykule wyprowadzeniach na zależności pozwalające optymalizować modele HMM wykorzystano metodę mnożników Lagrange'a.
    Modeling the speech signal with the use of hidden Markov models HMM constitutes one of the most effective ways of speech recognition. This article is devoted to mathematical bases of the theory of hidden Markov models. Special attention was paid in it to derivation of dependencies allowing appłying the models HMM for modeling signals. In the first part of the article there was presented derivation of dependencies allowing selection of parameters of the model of Markov process. In the further part of the article there are presented derivations of dependencies allowing sełecting parameters of the model of Markov chain in a stage way. The described methods are based on minimization of the probability of generating random-in-time sequence of observation in the function of parameters of the model. The method of Lagrange's multipliers was used in the derivations for dependencies, presented in the article, allowing optimizing the models HMM.
    Źródło:
    Elektrotechnika i Elektronika; 2005, 24, 2; 172-182
    1640-7202
    Pojawia się w:
    Elektrotechnika i Elektronika
    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
      Wyświetlanie 1-8 z 8

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