Feeling of co-presence in VR depends on the realism of virtual agents. Our study explores how three dimensions of realism—visual appearance, behavior, and interactability—affect co-presence and Orienting Response (OR), measured using heart rate (HR) and skin conductance response (SCR). Moreover, we test whether HR and SCR can be used as measures of psychological concepts that describe virtual interactions like co-presence. Fourty-five participants passively viewed virtual characters while their HR and SCR were recorded. Afterwards participants assessed the experience of interacting with the virtual agents. The interactability of the virtual characters increased co-presence, and so did heightened appearance realism, but only when the level of behavioral realism was high. High visual and behavioral realism led to increase in SCR while visual realism alone evoked deeper HR deceleration. Nonetheless, neither SCR nor HR correlated with any psychological concepts that describe virtual interactions. In conclusion, realism can increase both the co-presence and magnitude of the OR, yet physiological indices can not reliably gauge the experience of interactions with virtual characters.

Monetization of some elements of the mechanics has been present for a while in the videogames industry, often as a part of the freemium model, where basic functionalities are free, and premium functionalities are paid. With the development of XR applications and Meta-verse, VR has become the next medium where microtransactions can be applicable. At the same time, voice user interfaces are gaining an increasing number of users and some part of them frequently use them for shopping (e.g. using smart speakers like Alexa), while the others do not feel comfortable taking the purchasing decisions using voice. In the NUX project, we merge VR environment with a conversational voice user interface based on the natural language. In this study, we present the results of the comparison of the graphical and vocal forms of microtransactions, using a qualitative methodology (N=12).

The paper presents an approach to automatic recognition of emotions in speech signals. The applied method bases on the composition of two discrete frequency transformations. The wavelet transform was calculated first and next the Fourier transform was applied. The Fourier-wavelet transform representation is used to find the differences between emotions in speech signals. A set of approximately 30 seconds long speech signals was used to verify the efficiency of presented methods. It gives the possibility of analyzing the performance of speech emotion recognition in the Fourier-wavelet domain.

An algorithm for automatic detection of breath events in a speech signal is suggested in this paper. The issues of breath events occurrences in recordings are discussed as well as their statistical parameters. Also the role of breath pauses for signalizing punctuation and emotional or physical state of the speaker, in both spontaneous and read speech, is described. Wavelet parameters of energy in frequency subbands are obtained from discrete packet wavelet decomposition with mel-scale as patterns of breaths. In the beginning of the detection procedure, preliminary hypotheses of breath are indicated in the analyzed speech signal using temporal features. Then, discrete wavelet transform parameters are calculated. Dynamic time warping is applied to establish final recognition. The algorithm will be used for automatic speech recognition for measuring similarity between training and test features vectors.