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Establishing speaker's age and sex
Rendek, Tomáš ; Pfeifer, Václav (referee) ; Atassi, Hicham (advisor)
This work deals with speaker´s age and gender recognition. At the beginning it introduces the practical usage of this application and discusses the solutions available. The theoretical part of the thesis specifies the feature extraction and reduction methods and speech databases used in the experiments. The practical part describes the recognizer implemented in the Emotional tool and in two chapters describes the individual experiments. Regarding speaker´s gender estimation; we focused on the impact of the emotional state and speaker's age on the classification process. The two remain experiments were dedicated for general gender estimation performed by using two different classifiers – GMM and k-NN. These two classifiers were used in age estimation as well. In this case, four Group of age was formed and two different feature sets namely: segmental and suprasegmental were exploited four groups
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Artificial intelligence in Behavioral Finance
Niyazli, Toghrul ; Ismayilov, Ilgar (advisor) ; Novák, Jiří (referee)
Since the dot.com bubble burst in 2001, financial markets have been plagued by extreme volatility caused by investors emotions and irrational behaviors. Since the financial market has become more complex and tech oriented, stock market sentiment has been a key determinant of large market movements. In this context, it is worth to look into the role of emotions such as fear, greed, and excitement in influencing investment decisions. In addition, newly developed artificial intelligence techniques can be utilized to record and collect data from online sources to conclude collective irrational human behaviors. Behavioral finance is a newly developed discipline that examines the impact of psychological elements on decision-making processes when individuals encounter uncertain situations. This thesis aims to analyze the influence of behavioral finance phenomena including Herding, Anchoring, and Overconfidence biases on the investment decision-making practices within the context of the United States stock market. One of the study goals is to investigate whether sentiment of public, can be a rational tool impacting on investors' decision- making process or not. The investigation is prompted by the potential of Artificial Intelligence and Machine Learning to accurately analyze vast amounts of data and make...
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Establishing speaker's age and sex
Rendek, Tomáš ; Pfeifer, Václav (referee) ; Atassi, Hicham (advisor)
This work deals with speaker´s age and gender recognition. At the beginning it introduces the practical usage of this application and discusses the solutions available. The theoretical part of the thesis specifies the feature extraction and reduction methods and speech databases used in the experiments. The practical part describes the recognizer implemented in the Emotional tool and in two chapters describes the individual experiments. Regarding speaker´s gender estimation; we focused on the impact of the emotional state and speaker's age on the classification process. The two remain experiments were dedicated for general gender estimation performed by using two different classifiers – GMM and k-NN. These two classifiers were used in age estimation as well. In this case, four Group of age was formed and two different feature sets namely: segmental and suprasegmental were exploited four groups
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