This work presents the development of a real-time framework for the research of multimodal feedback of robots/talking agents in the context of Human Robot Interaction (HRI) and Human Computer Interaction (HCI). For evaluating the framework, a Multimodal corpus is built (ENTERFACE_STEAD), and a study on the important multimodal features was done for building an active Robot/Agent listener of a storytelling experience with Humans. The experiments show that even when building the same reactive behavior models for Robot and Talking Agents, the interpretation and the realization of the behavior communicated is different due to the different communicative channels Robots/Agents offer be it physical but less human-like in Robots, and virtual but more expressive and human-like in Talking agents.

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.

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.

Large databases of scanned documents (medical records, legal texts, historical documents) require natural language processing for retrieval and structured information extraction. Errors caused by the optical character recognition (OCR) system increase ambiguity of recognized text and decrease performance of natural language processing. The paper proposes OCR post correction system with parametrized string distance metric. The correction system learns specific error patterns from incorrect words and common sequences of correct words. A smoothing technique is proposed to assign non-zero probability to edit operations not present in the training corpus. Spelling correction accuracy is measured on database of OCR legal documents in English language. Language model and learning string metric with smoothing improves Viterbi-based search for the best sequence of corrections and increases performance of the spelling correction system.

The SCORPIO is a small-size mini-teleoperator mobile service robot for booby-trap disposal. It can be manually controlled by an operator through a portable briefcase remote control device using joystick, keyboard and buttons. In this paper, the speech interface is described. As an auxiliary function, the remote interface allows a human operator to concentrate sight and/or hands on other operation activities that are more important. The developed speech interface is based on HMM-based acoustic models trained using the SpeechDatE-SK database, a small-vocabulary language model based on fixed connected words, grammar, and the speech recognition setup adapted for low-resource devices. To improve the robustness of the speech interface in an outdoor environment, which is the working area of the SCORPIO service robot, a speech enhancement based on the spectral subtraction method, as well as a unique combination of an iterative approach and a modified LIMA framework, were researched, developed and tested on simulated and real outdoor recordings.

In this paper we investigate the capacity of sound & timing information during typing of a password for the user identification and authentication task. The novelty of this paper lies in the comparison of performance between improved timing-based and audio-based keystroke dynamics analysis and the fusion for the keystroke authentication. We collected data of 50 people typing the same given password 100 times, divided into 4 sessions of 25 typings and tested how well the system could recognize the correct typist. Using fusion of timing (9.73%) and audio calibration scores (8.99%) described in the paper we achieved 4.65% EER (Equal Error Rate) for the authentication task. The results show the potential of using Audio Keystroke Dynamics information as a way to authenticate or identify users during log-on.