Artificial neural networks in text recognition

Tema ovog završnog rada je upotreba umjetnih neuronskih mreža pri prepoznavanju rukom pisanih brojeva i teksta. Uvodno poglavlje daje uvid u sam problem, te ukratko opisuje strukturu i podjelu umjetnih neuronskih mreža. U drugom poglavlju je sam problem prepoznavanja brojki i slova detaljnije opisan, i uz njega su prikazana područja primjena u kojima se danas koriste takvi algoritmi. Zatim je provedena analiza najčešće korištenih algoritama u opisanom području istraživanja. Algoritmi dubokog učenja korišteni u programskoj podršci ovog rada su detaljnije obrađeni u trećem poglavlju, a za ostale suvremene algoritme je dan pregled visoke razine te je ukratko opisan njihov način rada. Konačno, u zadnjem poglavlju su izneseni rezultati rada različitih struktura umjetnih neuronskih mreža za rješavanje navedenog problema koristeći varijacije značajnih parametara. Modeli spomenutih struktura kreirani su u programskom paketu Python koristeći biblioteku TensorFlow uz razne dodatne biblioteke.The topic of this thesis is the application of artificial neural networks in handwritten character and digit recognition. The problem is presented in the introductory chapter, along with types and structures of artificial neural networks. Second chapter gives a deeper insight to the problem of digit and character recognition, and describes contemporary applications of such technologies. The analysis of most commonly used algorithms in the described field of research is given afterwards. Deep learning methods used in this thesis are thoroughly analyzed in the third chapter, along with a high level overview of other contemporary algorithms and their operating principles. Finally, the last chapter contains the results of different structures of neural networks for the given problem with variations to the most important parameters. Models of previously mentioned structures are created in the programming language Python combined with a machine learning library TensorFlow, along with various additional libraries

Optimization of automatic speech emotion recognition systems

Osnov za uspešnu integraciju emocionalne inteligencije u sofisticirane sisteme veštačke inteligencije jeste pouzdano prepoznavanje emocionalnih stanja, pri čemu se paralingvistički sadržaj govora izdvaja kao posebno značajan nosilac informacija o emocionalnom stanju govornika. U ovom radu je sprovedena komparativna analiza obeležja govornog signala i klasifikatorskih metoda najčešće korišćenih u rešavanju zadatka automatskog prepoznavanja emocionalnih stanja govornika, a zatim su razmotrene mogućnosti popravke performansi sistema za automatsko prepoznavanje govornih emocija. Izvršeno je unapređenje diskretnih skrivenih Markovljevih modela upotrebom QQ krive za potrebe određivanja etalona vektorske kvantizacije, a razmotrena su i dodatna unapređenja modela. Ispitane su mogućnosti vernije reprezentacije govornog signala, pri čemu je analiza proširena na veliki broj obeležja iz različitih grupa. Formiranje velikih skupova obeležja nameće potrebu za redukcijom dimenzija, gde je pored poznatih metoda analizirana i alternativna metoda zasnovana na Fibonačijevom nizu brojeva. Na kraju su razmotrene mogućnosti integracije prednosti različitih pristupa u jedinstven sistem za automatsko prepoznavanje govornih emocija, tako da je predložena paralelna multiklasifikatorska struktura sa kombinatornim pravilom koje pored rezultata klasifikacije pojedinačnih klasifikatora ansambla koristi i informacije o karakteristikama klasifikatora. Takođe, dat je predlog automatskog formiranja ansambla klasifikatora proizvoljne veličine upotrebom redukcije dimenzija zasnovane na Fibonačijevom nizu brojevaThe basis for the successful integration of emotional intelligence into sophisticated systems of artificial intelligence is the reliable recognition of emotional states, with the paralinguistic content of speech standing out as a particularly significant carrier of information regarding the emotional state of the speaker. In this paper, a comparative analysis of speech signal features and classification methods most often used for solving the task of automatic recognition of speakers' emotional states is performed, after which the possibilities for improving the performances of the systems for automatic recognition of speech emotions are considered. Discrete hidden Markov models were improved using the QQ plot for the purpose of determining the codevectors for vector quantization, and additional models improvements were also considered. The possibilities for a more faithful representation of the speech signal were examined, whereby the analysis was extended to a large number of features from different groups. The formation of big sets of features imposes the need for dimensionality reduction, where an alternative method based on the Fibonacci sequence of numbers was analyzed, alongside known methods. Finally, the possibilities for integrating the advantages of different approaches into a single system for automatic recognition of speech emotions are considered, so that a parallel multiclassifier structure is proposed with a combinatorial rule, which, in addition to the classification results of individual ensemble classifiers, uses information about classifiers' characteristics. A proposal is also given for the automatic formation of an ensemble of classifiers of arbitrary size by using dimensionality reduction based on the Fibonacci sequence of numbers