Standardized speech quality assessments should optimally occur in soundproof environments with minimal noise interference. However, poor ventilation in such environments can lead to increased CO2concentrations, which may affect cognitive function and judgement. This study investigates whether CO2levels affect speech quality ratings and response behaviour in listening tests. Across over 5,000 trials with 24 participants, we found three key effects: (1) higher CO2levels were associated with faster response times, indicating more impulsive decision-making; (2) ratings of poor-quality samples became more favorable, and high-quality samples less favorable under high CO2, suggesting reduced perceptual discrimination; (3) rating variability decreased, implying diminished sensitivity. These findings highlight a previously overlooked factor in speech quality research: ambient air quality may introduce bias into subjective ratings.

Objectively measuring affective states remains a recurring challenge in psychology and user experience research. A promising proxy for perceived arousal is the momentary assessment of the pupil diameter. However, the pupillometric signal is highly susceptible to variations in stimulus luminance, which can substantially confound the results. We introduce a method to improve the accuracy of pupillometric signals as an arousal predictor by eliminating frame-by-frame luminance variations in visual stimuli (i.e. scaling the brightness value for all pixels such that each frame has the same average luminance). This process was termed "equalization". We tested our approach in a study with 31 participants between the ages of 21 and 64. Our study focused on two metrics: (1) the perceived quality of the stimuli should not be affected by the process, and (2) the predictive power of the pupillometric signal for the perceived arousal should be greater for the equalized videos than for the non-equalized ones. We used a within-subjects design, where each participant rated their affective response and five distinct quality dimensions of four videos (two in the equalized and two in the non-equalized condition). Our results indicate that the equalization process does improve the predictive power of the pupillometric signal by a substantial amount. Statistical analyses also show that the participants rated all quality dimensions equivalently for an equalized and non-equalized variant of the same stimulus, with only small differences that are limited to a subset of the perceptual dimensions. While potentially limiting the efficacy of the process in some scenarios, the strengthened explanatory power of the pupillometric signal leaves room for a wide range of possible applications.

The EyeSight feature, introduced with the new Apple Vision Pro XR headset, promises to revolutionize user interaction by simulating real human eye expressions on a digital display. This feature could enhance XR devices’ social acceptability and social presence when communicating with others outside the XR experience. In this pilot study, we explore the implications of the EyeSight feature by examining social acceptability, social presence, emotional responses, and technology acceptance. Eight participants engaged in conversational tasks in three conditions to contrast experiencing the Apple Vision Pro with EyeSight, the Meta Quest 3 as a reference XR headset, and a face-to-face setting. Our preliminary findings indicate that while the EyeSight feature improves perceptions of social presence and acceptability compared to the reference headsets, it does not match the social connectivity of direct human interactions.

This manuscript investigates the suitability of video-based conversation simulations for studying human reactions to quality degradations, focusing on facial configurations as a proxy for QoE. We analyze data from two distinct studies: a video-simulated video-telephony scenario using the storytime dataset, where participants passively watched videos, and a second study involving real conversations between participants. In both studies, facial features were continuously recorded using Apple's iOS ARKit API. We identify a factor structure of facial features that significantly relates to participants' QoE ratings in the first study and validate its robustness by replicating it in the second, independent study. Our findings suggest statistically significant estimations of QoE ratings across both paradigms, demonstrating the suitability of passive conversation simulations for studying human reactions to quality degradation. We assess the value of the proposed approach at its present stage and conclude that it can be a valuable tool when used in conjunction with other methods, as its predictive capabilities are still not robust enough to rely solely on this analysis technique.

The subjective quality of experience (QoE) in multimedia contexts is influenced by various factors, including individual differences among raters and experimental setups. While the latter has been extensively studied, the former remains relatively unexplored. This paper investigates the impact of hangriness - a mental state of irritability and frustration caused by hunger - on QoE ratings. In our analysis, hangriness appears to be prevalent in a specific time interval, where individuals have not consumed any food between five and eleven hours. Our analysis, comprising ratings from 100 participants, reveals a significant, non-linear effect of hangriness on QoE ratings, specifically for multimedia stimuli with subpar quality. Participants in the hangry state rated such stimuli significantly worse compared to those who had eaten recently or abstained from food for more than eleven hours. Interestingly, this effect was not observed for high-quality multimedia content. Our findings highlight the importance of considering individual differences, such as hangriness, in QoE research, as they can significantly impact subjective ratings. Further research is needed to corroborate these results and explore other factors that may influence QoE ratings. This work contributes to a better understanding of individual variability in multimedia quality perception and provides insights for designing more reliable QoE assessment methods.

With the advances in speech communication systems such as online conferencing applications, we can seamlessly work with people regardless of where they are. However, during online meetings, speech quality can be significantly affected by background noise, reverberation, packet loss, network jitter, etc. Because of its nature, speech quality is traditionally assessed in subjective tests in laboratories and lately also in crowdsourcing following the international standards from ITU-T Rec. P.800 series. However, those approaches are costly and cannot be applied to customer data. Therefore, an effective objective assessment approach is needed to evaluate or monitor the speech quality of the ongoing conversation. The ConferencingSpeech 2022 challenge targets the non-intrusive deep neural network models for the speech quality assessment task. We open-sourced a training corpus with more than 86K speech clips in different languages, with a wide range of synthesized and live degradations and their corresponding subjective quality scores through crowdsourcing. 18 teams submitted their models for evaluation in this challenge. The blind test sets included about 4300 clips from wide ranges of degradations. This paper describes the challenge, the datasets, and the evaluation methods and reports the final results.