Jozef Juhár

This paper presents a comprehensive evaluation and comparison of selected conventional and advanced approaches for detecting and classifying emotional stress levels from speech. Three representative and publicly available stress datasets – CRISIS, StressDat, and SUSAS – were utilized. A detailed comparative analysis of their speech characteristics, including fundamental frequency (F₀), formant frequencies (F₁, F₂, F₃), voice intensity, harmonic-to-noise ratio, jitter, and shimmer, was conducted to investigate how these features change with increasing stress and contribute to its detection. Both conventional feature-based methods and deep learning techniques, including transfer and self-supervised learning, are explored in the experimental part of this research. The best classification results among the conventional methods were achieved using a combination of features extracted via the Discrete Wavelet Transform and GammaTone Cepstral Coefficients (DWT+GTCC), paired with a Subspace k-Nearest Neighbors classifier, which yielded F1-scores of up to 91.1% on CRISIS and 82.4% on StressDat. Among the deep learning approaches, we fine-tuned self-supervised models such as Wav2Vec 2.0 and BYOL-S (Bootstrap Your Own Latent for Speech), as well as transfer learning models including VGGish and YAMNet.Wav2Vec 2.0 consistently outperformed the others, achieving an F1-score of 94% on CRISIS and over 87% on StressDat. On the SUSAS dataset, which contains speech under simulated and real stress conditions, an F1-score of 77% was achieved using both the fine-tuned Wav2Vec 2.0 model as well as a pipeline combining BYOL-S feature extraction with a Support Vector Machine (SVM) classifier. After merging all stress-related datasets into a single balanced dataset,Wav2Vec 2.0 achieved the highest F1-score of 85%. Incorporating additional emotional speech data improved robustness and led to a slight increase in the accuracy of classifying stress into three distinct levels. These results highlight the importance of robust feature selection, appropriate classifier design, and the integration of emotional speech data to enhance generalization performance in real-world applications.

The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the “SentiSK” dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The “SentiSK” dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the “SentiSK” and “Sentigrade” datasets involved three classes (positive, neutral, and negative). In the second task, testing on the “SentiSK”, “Sentigrade”, and “Slovak dataset for SA” datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%.

The paper aims to study speaker and listener behavior in dyadic speech communication. A multimodal (speech and video) corpus of dyadic face-to-face conversations on various topics was created. The corpus was manually labeled on several layers (text transcription, backchannel modality and function, POS tags, prosody, and gaze). The statistical analysis was done on the proposed corpus. We focused on backchannel inviting cues on the speaker side and backchannels on the listener side and their patterns. We aimed to study interlocutor backchannel behavior and backchannel-related signals. The results of the analysis show similar patterns in the case of backchannel inviting cues between Slovak and English data and highlight the importance of gaze direction in a face-to-face speech communication scenario. The described corpus and results of the analysis are one of the first steps leading towards natural artificial intelligence-driven human–computer speech conversation.

SK-QuAD is the first manually annotated dataset of questions and answers in Slovak. It consists of more than 91k factual questions and answers from various fields. Each question has an answer marked in the corresponding paragraph. It also contains negative examples in the form of "unanswered questions" and "plausible answers". The dataset is published free of charge for scientific use. We aim to contribute to the creation of Slovak or multilingual systems for generating an answer to a question in a natural language. The paper provides an overview of the existing datasets for question answering. It describes the annotation process and statistically analyzes the created content. The dataset expands the possibilities of training and evaluation of multilingual language models. Experiments show that the dataset achieves state-of-the-art results for Slovak and improves question answering for other languages in zero-shot learning. We compare the effect of machine-translated data with manually annotated. Additional data improve the modeling for low-resourced languages.

In the article, we describe recent trends in the detection of hate speech and offensive language on social media. We accord from the latest studies and scientific contributions. The article describes current trends and the most used methods in connection with the detection of hate speech and offensive language. At the same time, we focus on the importance of emoticons, hashtags, and swearing in the field of social networks. We point out the topicality of the selected topic, describe the next direction of our work, and suggest possible solutions to current problems in this field of research.

In this paper, research focused on speech activity detection using brain EEG signals is presented. In addition to speech stimulation of brain activity, an innovative approach based on the simultaneous stimulation of the brain by visual stimuli such as reading and color naming has been used. Designing the solution, classification using two types of artificial neural networks were proposed: shallow Feed-forward Neural Network and deep Convolutional Neural Network. Experimental results of classification demonstrated F1 score 79.50% speech detection using shallow neural network and 84.39% speech detection using deep neural network based on cross-evaluated classification models.

Nowadays, there are increasing demands on the efficiency of data transmission. Increased data transmission efficiency can be ensured through 5G networks and the services they offer. In this paper, we simulate LTE-A, enhanced Mobile BroadBand, and enhanced Mobile BroadBand services with millimeter waves in scenarios with different end-users and analyze the achieved results of delay, throughput, and jitter. Achieved results are obtained with NS-3. This article thoroughly describes and implements the mmWave module for NS-3 because it is an essential part of this and future work. The paper ends with an illustration of yielded results and presents some possible topics for future work.

With the ever-progressing development in the field of computational and analytical science the last decade has seen a big improvement in the accuracy of electroencephalography (EEG) technology. Studies try to examine possibilities to use high dimensional EEG data as a source for Brain to Computer Interface. Applications of EEG Brain to computer interface vary from emotion recognition, simple computer/device control, speech recognition up to Intelligent Prosthesis. Our research presented in this paper was focused on the study of the problematic speech activity detection using EEG data. The novel approach used in this research involved the use visual stimuli, such as reading and colour naming, and signals of speech activity detectable by EEG technology. Our proposed solution is based on a shallow Feed-Forward Artificial Neural Network with only 100 hidden neurons. Standard features such as signal energy, standard deviation, RMS, skewness, kurtosis were calculated from the original signal from 16 EEG electrodes. The novel approach in the field of Brain to computer interface applications was utilised to calculated additional set of features from the minimum phase signal. Our experimental results demonstrated F1 score of 86.80% and 83.69% speech detection accuracy based on the analysis of EEG signal from single subject and cross-subject models respectively. The importance of these results lies in the novel utilisation of the mobile device to record the nerve signals which can serve as the stepping stone for the transfer of Brain to computer interface technology from technology from a controlled environment to the real-life conditions.

During previous years, researchers from TUKE (Technical University of Kosice) and MSU (Mississippi State University) collaborated on building a virtual immersive training environment for law enforcement officers. The training tool was designed for operation in Virtual Reality (VR) using HMD (Head Mounted Display) and 3D Desktop modes. The goal was to control a Jaguar V4 robot in a virtual environment using both joystick and voice commands, because not all functions could be accessed by hand controllers alone. The speech recognition was designed for English speakers. Firstly it was tested at MSU with five US participants and later the same tests were repeated with 16 students at TUKE, who were non-native English speakers. This article provides an overview of the experiment and a comparison of cognitive aspects and user acceptance levels resulted from these trials on different continents and different English speakers (native and non-native ones).

This paper describes the development of the speech audiometry application for pediatric patients in Slovak language and experiences obtained during testing with healthy children, hearing-impaired children, and elderly persons. The first motivation behind the presented work was to reduce the stress and fear of the children, who must undergo postoperative audiometry, but over time, we changed our direction to the simple game-like mobile application for the detection of possible hearing problems of children in the home environment. Conditioned play audiometry principles were adopted to create a speech audiometry application, where children help the virtual robot Thomas assign words to pictures; this can be described as a speech recognition test. Several game scenarios together with the setting condition issues were created, tested, and discussed. First experiences show a positive influence on the children’s mood and motivation.