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The relation of emotions and intonation curves
Gavlasová, Radka ; Smékal, Zdeněk (referee) ; Tučková,, Jana (advisor)
This thesis deals with intonation curves and their relation to human emotions. Besides the theoretical part where you can learn about speech production, signal processing and psychological distribution of emotions, there is also a unique database recorded with the help of two professional actors. The main goal of this thesis is to classify created data using artificial neural networks into four classes. Those classes are anger, joy, boredom and sadness. The practical part was implemented in a programming platform called Matlab using Classification Learner app. Features used for this method were variations of fundamental frequency and MFCC. The results were compared with a listening survey so that it could be determined whether the results provided by neural network are relevant to some kind of a human factor. Success rate of the trained models reached 82 %, new data testing reached 75 %. Listening survey confirmed that the results correspond to the assumption of human perception. Better success rate would be accomplished by using a bigger set of higher quality data.
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Speech-signal-based recognition of type of transmission channel
Kopřiva, Tomáš ; Burget, Radim (referee) ; Atassi, Hicham (advisor)
This work deals with the classification of five different transmission channels by speech signal processing. The channels considered are: GSM, two PSTN channels and two VoIP channels. For the training and testing purposes, a speech database for the transmission channels called SPLAB_TranCh was constructed. The speech signals of this corpus originally come from well-known TIMIT database, where each utterance passed through each mentioned transmission channel. The main objective of this work is to find optimal features and classification accuracy that yield best classification accuracy. Several types of features, including MFCC, LPCC and spectral characteristics were put under examination. The best suprasegmental features were identified by using mRMR algorithm. Several classifiers were tested as well. The results suggested that the classification of transmission channel can be performed with high accuracy (around 90 %). Influence of adverse effects, which can occur during transmission, is also examined. Considered types of distortions are: saturation, thresholding, echo, crackling noises and different colors of noises and filters.
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Controlling and Measuring Sport Drills by Voice/Sound
Odehnal, Jiří ; Křivka, Zbyněk (referee) ; Rychlý, Marek (advisor)
This master's thesis deals with the design and development of mobile aplication for Android platform. The aim of the work is to implement a simple and user-friendly user interface that would support and assist the user in trainning and sport exercises. The thesis also include implementation of sound detection to support during exercises and voice instruction by application. In practice the application should help in making training exercises more comfortable without the user being forced to keep mobile device in hand.
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Speech Recognition For Selected Languages
Schmitt, Jan ; Karafiát, Martin (referee) ; Janda, Miloš (advisor)
This bachelor's thesis deals with recognition of continues speech for three languages - Bulgarian, Croatian and Swedish. There are described basics of speech processing and recognition methods like acoustic modeling using hidden Markov models and gaussian mixture models. Another aim of this work is preparing data for those languages from GlobalPhone database, so they may be used with speech recognition toolkits Kaldi and HTK. With data prepared there are several models trained and tested using Kaldi toolkit.
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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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Deep learning based sound records analysis
Kramář, Denis ; Říha, Kamil (referee) ; Přinosil, Jiří (advisor)
This master thesis deals with the problem of audio-classification of the chainsaw logging sound in natural environment using mainly convolutional neural networks. First, a theory of grafical representation of audio signal is discussed. Following part is devoted to the machine learning area. In third chapter, some of present works dealing with this problematics are given. Within the practical part, used dataset and tested neural networks are presented. Final resultes are compared by achieved accuracy and by ROC curves. The robustness of the presented solutions was tested by proposed detection program and evaluated using objective criteria.
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Emotional State Recognition and Classification Based on Speech Signal Analysis
Černý, Lukáš ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
The diploma thesis focuses on classification of emotions. Thesis deals about parameterization of sounds files by suprasegment and segment methods with regard for next used of these methods. Berlin database is used. This database includes many of sounds records with emotions. Parameterization creates files, which are divided to two parts. First part is used for training and second part is used for testing. Point of interest is self-organization network. Thesis includes Matlab´s program which can be used for parameterization of any database. Data are classified by self-organization network after parameterization. Results of hits rates are presented at the end of this diploma thesis.
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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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Real-time voice command recognition system
Šíbl, Evžen ; Kiac, Martin (referee) ; Přinosil, Jiří (advisor)
The bachelor thesis deals with the development of a system for voice command recognition. The classifier of this system was created using a neural network. In this thesis you will learn about the history and problems of speech recognition. A system has been created that detects a section in a recording containing a speech signal, which then uses the classifier to decide what word from the word table it is. Three models with the same architecture but with different training data were created. These models were then compared with each other. A simple user interface was created for the resulting system.
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Logopedic defect analysis and recognition in speech utterances
Diviš, Jan ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
This bachelor's thesis deals with logopaedia mistake called dyslalie and its characteristics. I described the process creation and representation of speech. There are presented bases of processing and analyses speech signal ( LPC, cepstral, MFCC). I presented characteristics of speech and calculation of LPC, cepstral and Mel-frequency cepstral coefficients in the programme MATLAB. The bachelor's thesis includes problems of incorrect pronunciation sound "r" and "ř".
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