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Recognition of Isolated Words for Electronic Dictionaries
Hrdlička, Pavel ; Szőke, Igor (referee) ; Grézl, František (advisor)
This work is concerned with creation of isolated word recognizer for electronic dictionaires, testing its functionality on data sample and improvement by normalisation and speaker adaptation techniques. Word recognizer is built on HTK (Hidden Markov Model Toolkit). At the beginning of this document, the main aims of the work are set. In the next chapter is theoretical analysis, which describes process of recognition of isolated words with hidden Markov models. Next chapter specifies the speech data, which were used for testing. Other resources for building recognizer, like models, dictionary and grammar are described in next chapter. Before creation of recognizer, it was necessary to solve conversion between the phonemes set which was used in dictionary and set, which uses the recognizer. The recognizer was built with 8~kHz models first, than 16~kHz models were also used. Normalisation and speaker adaptation techniques were used. Obtained data were processed and results are analyzed in separate chapter. Finally is discussed, if the goals of the work were reached and what are the next steps of application development.
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Speech Recognition (digit)
Kantar, Martin ; Minář, Petr (referee) ; Matoušek, Radomil (advisor)
The aim of this diploma thesis is to explain what speech is and what are its constituents. I mention commonly used methods which are used for preparation of signals which we use for recognition. Schematic examples show principles of current recognizers of speech, their advantages and disadvantages. I made speech recognition program for 0-9 numerals in Matlab for neural nets learning.
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Digital terrestrial television broadcasting DVB-T/H and DVB-T2
Pospíchal, Martin ; Ulovec,, Karel (referee) ; Kratochvíl, Tomáš (advisor)
Master's thesis compares the standard for digital terrestrial television broadcasting of the first generation DVB-T/H and the second generation DVB-T2 with particular emphasis on the modulator, a security channel interference, the signal from the transmitting environment itself and the modulation signal. The following description of specific models of transmission channels for fixed, portable and mobile reception of digital terrestrial signal. Comparison with the particular relates of the transmission parameters for different types of reception of digital terrestrial television with achieving efficiency and effectiveness of transmission at the level of laboratory measurements and computer simulation.
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Keyword Spotting Implementation to Mobil Phone (Symbian 60)
Cipr, Tomáš ; Schwarz, Petr (referee) ; Szőke, Igor (advisor)
Keyword spotting is one of the many applications of automatic speech recognition. Its purpose is determining spots in given utterance in which some of the specified words were spoken. Keyword spotting has a great potential to enhance performance of new applications as well as the existing ones. An example could be a mobile phone voice control. Due to OS Symbian's coming to the market it is even possible for end user to implement a keyword spotting for a mobile phone on his or her own. The thesis describes theoretical prerequisites for keyword spotting and its implementation. Firstly the OS Symbian is presented with respect to the given task. Secondly each step of keyword spotting process is described. Finally the object design of keyword spotter is presented followed by implementation description. The thesis concludes with results review and notes on possible improvements.
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Analysis of Parkinson's disease using segmental speech parameters
Mračko, Peter ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
This project describes design of the system for diagnosis Parkinson’s disease based on speech. Parkinson’s disease is a neurodegenerative disorder of the central nervous system. One of the symptoms of this disease is disability of motor aspects of speech, called hypokinetic dysarthria. Design of the system in this work is based on the best known segmental features such as coefficients LPC, PLP, MFCC, LPCC but also less known such as CMS, ACW and MSC. From speech records of patients affected by Parkinson’s disease and also healthy controls are calculated these coefficients, further is performed a selection process and subsequent classification. The best result, which was obtained in this project reached classification accuracy 77,19%, sensitivity 74,69% and specificity 78,95%.
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Application of statistical analysis of speech in patients with Parkinson's disease
Bijota, Jan ; Mžourek, Zdeněk (referee) ; Galáž, Zoltán (advisor)
This thesis deals with speech analysis of people who suffer from Parkinson’s disease. Purpose of this thesis is to obtain statistical sample of speech parameters which helps to determine if examined person is suffering from Parkinson’s disease. Statistical sample is based on hypokinetic dysarthria detection. For speech signal pre-processing DC-offset removal and pre-emphasis are used. The next step is to divide signal into frames. Phonation parameters, MFCC and PLP coefficients are used for characterization of framed speech signal. After parametrization the speech signal can be analyzed by statistical methods. For statistical analysis in this thesis Spearman’s and Pearson’s correlation coefficients, mutual information, Mann-Whitney U test and Student’s t-test are used. The thesis results are the groups of speech parameters for individual long czech vowels which are the best indicator of the difference between healthy person and patient suffering from Parkinson’s disease. These result can be helpful in medical diagnosis of a patient.
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Application of statistical analysis of speech in patients with Parkinson's disease
Bijota, Jan ; Mžourek, Zdeněk (referee) ; Galáž, Zoltán (advisor)
This thesis deals with speech analysis of people who suffer from Parkinson’s disease. Purpose of this thesis is to obtain statistical sample of speech parameters which helps to determine if examined person is suffering from Parkinson’s disease. Statistical sample is based on hypokinetic dysarthria detection. For speech signal pre-processing DC-offset removal and pre-emphasis are used. The next step is to divide signal into frames. Phonation parameters, MFCC and PLP coefficients are used for characterization of framed speech signal. After parametrization the speech signal can be analyzed by statistical methods. For statistical analysis in this thesis Spearman’s and Pearson’s correlation coefficients, mutual information, Mann-Whitney U test and Student’s t-test are used. The thesis results are the groups of speech parameters for individual long czech vowels which are the best indicator of the difference between healthy person and patient suffering from Parkinson’s disease. These result can be helpful in medical diagnosis of a patient.
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Recognition of Isolated Words for Electronic Dictionaries
Hrdlička, Pavel ; Szőke, Igor (referee) ; Grézl, František (advisor)
This work is concerned with creation of isolated word recognizer for electronic dictionaires, testing its functionality on data sample and improvement by normalisation and speaker adaptation techniques. Word recognizer is built on HTK (Hidden Markov Model Toolkit). At the beginning of this document, the main aims of the work are set. In the next chapter is theoretical analysis, which describes process of recognition of isolated words with hidden Markov models. Next chapter specifies the speech data, which were used for testing. Other resources for building recognizer, like models, dictionary and grammar are described in next chapter. Before creation of recognizer, it was necessary to solve conversion between the phonemes set which was used in dictionary and set, which uses the recognizer. The recognizer was built with 8~kHz models first, than 16~kHz models were also used. Normalisation and speaker adaptation techniques were used. Obtained data were processed and results are analyzed in separate chapter. Finally is discussed, if the goals of the work were reached and what are the next steps of application development.
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Keyword Spotting Implementation to Mobil Phone (Symbian 60)
Cipr, Tomáš ; Schwarz, Petr (referee) ; Szőke, Igor (advisor)
Keyword spotting is one of the many applications of automatic speech recognition. Its purpose is determining spots in given utterance in which some of the specified words were spoken. Keyword spotting has a great potential to enhance performance of new applications as well as the existing ones. An example could be a mobile phone voice control. Due to OS Symbian's coming to the market it is even possible for end user to implement a keyword spotting for a mobile phone on his or her own. The thesis describes theoretical prerequisites for keyword spotting and its implementation. Firstly the OS Symbian is presented with respect to the given task. Secondly each step of keyword spotting process is described. Finally the object design of keyword spotter is presented followed by implementation description. The thesis concludes with results review and notes on possible improvements.
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Analysis of Parkinson's disease using segmental speech parameters
Mračko, Peter ; Mekyska, Jiří (referee) ; Smékal, Zdeněk (advisor)
This project describes design of the system for diagnosis Parkinson’s disease based on speech. Parkinson’s disease is a neurodegenerative disorder of the central nervous system. One of the symptoms of this disease is disability of motor aspects of speech, called hypokinetic dysarthria. Design of the system in this work is based on the best known segmental features such as coefficients LPC, PLP, MFCC, LPCC but also less known such as CMS, ACW and MSC. From speech records of patients affected by Parkinson’s disease and also healthy controls are calculated these coefficients, further is performed a selection process and subsequent classification. The best result, which was obtained in this project reached classification accuracy 77,19%, sensitivity 74,69% and specificity 78,95%.
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