## 1 Introduction  
The diversity of the human individual has an impact on many aspects of our lives. Biological, cultural, or national diversity, especially in the process of interpersonal communication, creates problems in mutual understanding and expressing emotions. Starting with the mundane problems of everyday life in relationships between partners or between children and parents, we often make mistakes in understanding the expressiveness of emotions and the behavior of the other person. Situations become more serious when, in the face of experiencing a tragedy or falling into various traumas, we lose the ability to express and, at the same time, understand human emotions. We even talk about a disease called alexithymiaia [1]. It is obvious that in such situations we seek help from specialists, but as we enter the digital world more and more, we also expect to understand contacts with artificial intelligence software or futuristic robots, which are slowly becoming our everyday life.  
Social robotics, which aims to create robots capable of interacting naturally with humans, is becoming increasingly advanced thanks to modern artificial intelligence technologies. One of the key elements of these interactions is emotion recognition, which allows robots to better understand the needs and reactions of users. Recent advances in artificial intelligence, especially in the field of deep learning and neural networks, have significantly improved the ability to recognize emotions through multimodal approaches. Such approaches integrate different data sources, such as images, sounds, and text, which allows for a more comprehensive understanding of emotions, taking into account facial expressions, tone of voice, biomedical data, and analysis of the content of utterances [2]. Despite these advances, the challenges associated with recognizing emotions in Polish, both in speech and text, remain significant [3]. AI models are often trained on English-language resources [4; 5], which leads to limited accuracy in the case of languages such as Polish, where grammatical complexity and specific phonetic features can make analysis difficult. A particular deficiency in the Polish context is the dearth of large, diverse datasets that could be used to effectively train emotion recognition models. In the case of speech, these challenges also include variability in accent, intonation, and dynamics of utterance, while in texts, it is particularly difficult to analyze emotions hidden in the subtleties of syntax and context.  
The main objective of this work is to evaluate the latest and most precise deep learning models for the problem of speech emotion analysis using Mel spectrograms on a dataset based on the Polish language. The primary goal of this work is to show the differences in emotion recognition in text and speech, and propose and implement a dual-track audio module based on the indicated algorithms in a multimodal system for analyzing the emotions of social robots. The system itself can become a specific tool for collecting data in Polish for the problem of recognizing emotions in speech. Thanks to the feedback, in which the user of this system assesses the correctness of the recognized emotion, it is possible to create a rich set of data and these data for which emotions are not artificially induced.