\chapter{Introduction}
\label{ch:introduction}

Currently, the field of human-computer interaction (HCI)
rapidly grows due to the ubiquitous presence of technology in human life. The interaction between man and machine is on a daily basis, and there is demand for more sophisticated methods that would enable a machine to communicate with people as they do among themselves. To unlock such advanced technology, the machine needs to have human-like properties that allow perception and processing of the human stimulus and generation of an appropriate response. 

Even though most of the interactions with machines are through touch devices (keyboard, mouse, smartphone display), the trend in the current HCI field is to adapt machines to communicate with people through speech as the main and most natural communication tool that is an essential part of their daily life. Its interactive and straightforward character makes it a perfect transmitter of information enabling people to express their thoughts in real-time. Through the speech, there are encoded also other non-verbal features that complete the context of the speaker's thoughts. One such non-verbal expression is the emotional footprint, which provides insights into the feelings of a speaker during communication. To create a machine that knows properly communicate, the awareness of emotional context is crucial. The machine can obtain this ability through analysis of speech in order to decode and obtain this context.

The research area that deals with gaining emotional information from human speech is called \textit{speech emotion recognition}. The practical reasons to consider this topic are e.g. the ability to adjust machine behaviour to correspond with human emotional state, providing better service in call centres or better understanding of the nature of emotions. Despite trying to find a universal solution for more than twenty years, there is no present system that would even approach human abilities in emotion perception, analysis and classification. The main reason for a such situation is the unclear phenomenon of emotions and their interpretation even from the human point of view. This work aims to contribute with an innovative solution that follows current trends in the research and achieves a comparable performance with the best systems so far.

 Chapter~\ref{ch:ser} is dedicated to the analysis of the speech emotion recognition field. It comprehends the basic psychological theory of emotions, emotional models and the impact of emotions on human speech. This chapter continues with the description of methods for preprocessing, feature extraction and classification with trends for each topic. The last sections are dedicated to current state-of-the-art solutions and a list of issues and problems that are not yet solved.

Chapter~\ref{ch:solution-proposal} contains a theoretical explanation of concepts necessary to understand the solution design based on the Audio Spectrogram Transformer neural network (AST). Then, Chapter~\ref{ch:implementation} describes the implementation of the proposed solution. Chapter~\ref{ch:results} explains the evaluation methodology, describes the set of performed experiments with obtained results and conducts discussion about them.
 

