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Recognition of isolated words
Ondruška, Jiří ; Švrček, Martin (referee) ; Kolářová, Jana (advisor)
Human speech recognition in biometric systems is an actual problem, which science intensively deals with. One of most used methods is the method of hidden Markovov’s models. Attention in isolated words recognition is focused on characteristic speech signal parameters obtaining, enabling most clear identification due to hiddem Markov model application. This work concentrates on biometric systems, its methods, and then is focused on isolated words recognition problems. The hidden Markov model recognition system with usage of some Matlab functions is designed. Concept focuses on characteristic speech signal parameters obtaining, code book making through vector quantization, words model training and finally probability of concrete model and obtained word similarity evaluation. Ratio for one speaker's spoken words recognition reaches 40%.
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Character recognition in the soundtrack with SOM
Malásek, Jan ; Honzík, Petr (referee) ; Honzík, Petr (referee) ; Pohl, Jan (advisor)
This bachelor´s thesis describes a history of neural networks evolution and their using in speech recognition systems and shows problems with working and learning neural networks. It presents three chosen systems for speech recognition including their evaluation in experiments, their advantages and disadvantages. It is also about human speech characteristics and systems of its recognition. The last part is focused on frequency spectrums of different types of vowels and gives instructions for programming neural networks using MATLAB.
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Speech Recognition Algorithms in FPGA/DSP
Urbiš, Oldřich ; Herout, Adam (referee) ; Szőke, Igor (advisor)
This master's thesis deals with design of speech recognition algorithms with consideration of target technology, which is platform combinating digital signal processing and field programmable gate array. Algorithms for speech recognition includes: feature extraction of Melfrequency cepstral coefficients, hidden Markov models and their evaluation by Viterbi algorithm.
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Handwriting Recognition
Zouhar, David ; Řezníček, Ivo (referee) ; Mlích, Jozef (advisor)
This diploma thesis deals with handwriting recognition in real-time. It describes the ways how the intput data are processed. It is also focused on the classi cation methods, which are used for the recognition. It especially describes hidden Markov models. It also present the evaluation of the success of the recognition based on implemented experiments. The alternative keyboard for MeeGo system was created for this thesis as well. The established system achieved the success above 96%.
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Recognition of Handwriting for Mobile Phones
Talaš, Vladimír ; Chalupníček, Kamil (referee) ; Schwarz, Petr (advisor)
The goal of this project is to create a mobile phone application, which can use phone camera to get a photography. This photography contains text, application has an ability to find a text, recognize all characters and send output as SMS. In this application there are implemented algorithms for text recognize from pictures based on Hidden Markovov Models. The particular stress is put on training of the model, to maximalise a succes of text recognition. There are some experiments model training with model variables, which are leading in better ability of text recognition. It was achieved a value of 97% succesfully recognized characters.
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Gesture Based Human-Computer Interface
Jaroň, Lukáš ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This masters thesis describes possibilities and principles of gesture-based computer interface. The work describes general approaches for gesture control. It also deals with implementation of the selected detection method of the hands and fingers using depth maps loaded form Kinect sensor. The implementation also deals with gesture recognition using hidden Markov models. For demonstration purposes there is also described implementation of a simple photo viewer that uses developed gesture-based computer interface. The work also focuses on quality testing and accuracy evaluation for selected gesture recognizer.
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Enhancing the effectiveness of automatic speech recognition
Zelinka, Petr ; Tučková,, Jana (referee) ; Nouza,, Jan (referee) ; Sigmund, Milan (advisor)
This work identifies the causes for unsatisfactory reliability of contemporary systems for automatic speech recognition when deployed in demanding conditions. The impact of the individual sources of performance degradation is documented and a list of known methods for their identification from the recognized signal is given. An overview of the usual methods to suppress the impact of the disruptive influences on the performance of speech recognition is provided. The essential contribution of the work is the formulation of new approaches to constructing acoustical models of noisy speech and nonstationary noise allowing high recognition performance in challenging conditions. The viability of the proposed methods is verified on an isolated-word speech recognizer utilizing several-hour-long recording of the real operating room background acoustical noise recorded at the Uniklinikum Marburg in Germany. This work is the first to identify the impact of changes in speaker’s vocal effort on the reliability of automatic speech recognition in the full vocal effort range (i.e. whispering through shouting). A new concept of a speech recognizer immune to the changes in vocal effort is proposed. For the purposes of research on changes in vocal effort, a new speech database, BUT-VE1, was created.
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