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State of the art speech features used during the Parkinson disease diagnosis
Bílý, Ondřej ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with the diagnosis of Parkinson's disease by analyzing the speech signal. At the beginning of this work there is described speech signal production. The following is a description of the speech signal analysis, its preparation and subsequent feature extraction. Next there is described Parkinson's disease and change of the speech signal by this disability. The following describes the symptoms, which are used for the diagnosis of Parkinson's disease (FCR, VSA, VOT, etc.). Another part of the work deals with the selection and reduction symptoms using the learning algorithms (SVM, ANN, k-NN) and their subsequent evaluation. In the last part of the thesis is described a program to count symptoms. Further is described selection and the end evaluated all the result.
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Very limited Vocabulary Speech Recognizer
Vystavěl, Kamil ; Míča, Ivan (referee) ; Sysel, Petr (advisor)
This bachelor thesis deals with the implementation of voice diagnostic method with limited number of recognized words in Matlab environment. Recognizer is designed for recognition of isolated words and is based on the dynamic programming method. This method is realized by the dynamic time warping algorithm (DTW). Features of the speech signal are calculated by methods of short-term analysis in time and frequency domain and by methods that are based on cepstral analysis and linear predictive analysis. The representation of the word, which is generated from its features, is suitable for quantifying the degree of similarity with the representation of another word. In order to achieve the highest degree of similarity, the dynamic time warping algorithm eliminates influence of fluctuation of the speech rate by non-linear normalization time axis of one of the compared words. The degree of the similarity of the two compared words is enumerated as the words’ distance. The representations of known words are stored in a word-book. The unknown word is compared with all words in the word-book and recognizer calculates distances between every known word and the unknown word. The unknown word is defined as identical with the known word that has the shortest distance to the unknown word. The successfulness depends mainly on the choice of the features.
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Speaker recognition
Kašpar, Ladislav ; Atassi, Hicham (referee) ; Sysel, Petr (advisor)
My bachelor thesis is devoted to the problem of speaker recognition. It includes the basic theory on this topic. The theory focuses on the calculation of parameters for speaker recognition and description of the procedure for speaker recognition. An application for speaker recognition has been written in Matlab. It uses techniques as frequency formants, cepstral coefficients and segmentation of the signal as the main parameters.
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Identification of persons via voice imprint
Mekyska, Jiří ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
This work deals with the text-dependent speaker recognition in systems, where just a few training samples exist. For the purpose of this recognition, the voice imprint based on different features (e.g. MFCC, PLP, ACW etc.) is proposed. At the beginning, there is described the way, how the speech signal is produced. Some speech characteristics important for speaker recognition are also mentioned. The next part of work deals with the speech signal analysis. There is mentioned the preprocessing and also the feature extraction methods. The following part describes the process of speaker recognition and mentions the evaluation of the used methods: speaker identification and verification. Last theoretically based part of work deals with the classifiers which are suitable for the text-dependent recognition. The classifiers based on fractional distances, dynamic time warping, dispersion matching and vector quantization are mentioned. This work continues by design and realization of system, which evaluates all described classifiers for voice imprint based on different features.
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Parkinson disease diagnosis using speech signal analysis
Karásek, Michal ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
The thesis deals with the recognition of Parkinson's disease from the speech signal. The first part refers to the principles of speech signals and speech signals by patients suffering from Parkinson's disease. Further, it continues to describe the issues of speech signals processing, basic symptoms used for diagnosis of Parkinson's disease (e. g. VAI, VSA, FCR, VOT etc.) and reduction of these symptoms. The next part focuses on a block diagram of the program for the diagnosis of Parkinson's disease. The main objective of this thesis is comparison of two methods of feature selection (mRMR and SFFS). For classification have selected two different methods were used. The first method is classification kNN and second method of classification is Gaussian mixture model (GMM).
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Detecting Stress in Speech
Šoltés, Samuel ; Beneš, Karel (referee) ; Grézl, František (advisor)
Stress influences people in several ways and can lead to decrease in performance and / or critical mistakes. Stress detection in speech measures the influence of stress in speech. The goal of this thesis is to offer a closer look at the impacts of stress, choose adequate parameters of speech which would manifest these impacts, implement their estimation and compare their results. The thesis contains description of stress and its effects on humans; glottal pulse, spectrum, fundamental frequency and formants as the parameters chosen for stress estimation; design and implementation of parameter value estimation from speech signal and obtained values of given parameters on two different databases.
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Detecting Stress in Speech
Šoltés, Samuel ; Beneš, Karel (referee) ; Grézl, František (advisor)
Stress influences people in several ways and can lead to decrease in performance and / or critical mistakes. Stress detection in speech measures the influence of stress in speech. The goal of this thesis is to offer a closer look at the impacts of stress, choose adequate parameters of speech which would manifest these impacts, implement their estimation and compare their results. The thesis contains description of stress and its effects on humans; glottal pulse, spectrum, fundamental frequency and formants as the parameters chosen for stress estimation; design and implementation of parameter value estimation from speech signal and obtained values of given parameters on two different databases.
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Speaker recognition
Kašpar, Ladislav ; Atassi, Hicham (referee) ; Sysel, Petr (advisor)
My bachelor thesis is devoted to the problem of speaker recognition. It includes the basic theory on this topic. The theory focuses on the calculation of parameters for speaker recognition and description of the procedure for speaker recognition. An application for speaker recognition has been written in Matlab. It uses techniques as frequency formants, cepstral coefficients and segmentation of the signal as the main parameters.
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Very limited Vocabulary Speech Recognizer
Vystavěl, Kamil ; Míča, Ivan (referee) ; Sysel, Petr (advisor)
This bachelor thesis deals with the implementation of voice diagnostic method with limited number of recognized words in Matlab environment. Recognizer is designed for recognition of isolated words and is based on the dynamic programming method. This method is realized by the dynamic time warping algorithm (DTW). Features of the speech signal are calculated by methods of short-term analysis in time and frequency domain and by methods that are based on cepstral analysis and linear predictive analysis. The representation of the word, which is generated from its features, is suitable for quantifying the degree of similarity with the representation of another word. In order to achieve the highest degree of similarity, the dynamic time warping algorithm eliminates influence of fluctuation of the speech rate by non-linear normalization time axis of one of the compared words. The degree of the similarity of the two compared words is enumerated as the words’ distance. The representations of known words are stored in a word-book. The unknown word is compared with all words in the word-book and recognizer calculates distances between every known word and the unknown word. The unknown word is defined as identical with the known word that has the shortest distance to the unknown word. The successfulness depends mainly on the choice of the features.
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Parkinson disease diagnosis using speech signal analysis
Karásek, Michal ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
The thesis deals with the recognition of Parkinson's disease from the speech signal. The first part refers to the principles of speech signals and speech signals by patients suffering from Parkinson's disease. Further, it continues to describe the issues of speech signals processing, basic symptoms used for diagnosis of Parkinson's disease (e. g. VAI, VSA, FCR, VOT etc.) and reduction of these symptoms. The next part focuses on a block diagram of the program for the diagnosis of Parkinson's disease. The main objective of this thesis is comparison of two methods of feature selection (mRMR and SFFS). For classification have selected two different methods were used. The first method is classification kNN and second method of classification is Gaussian mixture model (GMM).
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