To meet best-practice standards, we are developing an interactive virtual avatar aiming as a training tool to raise interviewing skills of child-welfare and law-enforcement professionals. Therefore, we present the “Ilma” avatar that recognizes interviewers’ behavior during open-ended, closed and leading questions, and which can automatically respond to the conversation. We conducted a user study in which master students (N=3) and child protective workers (N=8)  interviewed “Ilma”  and rated their perception of the interaction. The results show that the participants valued the interaction and found the avatar useful. Thus, it has great potential to be an effective training tool.

Traditionally, the Quality of Experience (QoE) is assessed in a controlled laboratory environment where participants give their opinion about the perceived quality of a stimulus on a standardized rating scale. Recently, the usage of crowdsourcing micro-task platforms for assessing the media quality is increasing. The crowdsourcing platforms provide access to a pool of geographically distributed, and demographically diverse group of workers who participate in the experiment in their own working environment and using their own hardware. The main challenge in crowdsourcing QoE tests is to control the effect of interfering influencing factors such as a user's environment and device on the subjective ratings. While in the past, the crowdsourcing approach was frequently used for speech and video quality assessment, research on a quality assessment for gaming services is rare. In this paper, we present a method to measure gaming QoE under typically considered system influence factors including delay, packet loss, and framerates as well as different game designs. The factors are artificially manipulated due to controlled changes in the implementation of games. The results of a total of five studies using a developed evaluation method based on a combination of the ITU-T Rec. P.809 on subjective evaluation methods for gaming quality and the ITU-T Rec. P.808 on subjective evaluation of speech quality with a crowdsourcing approach will be discussed. To evaluate the reliability and validity of results collected using this method, we finally compare subjective ratings regarding the effect of network delay on gaming QoE gathered from interactive crowdsourcing tests with those from equivalent laboratory experiments.

The gaming industry is one of the largest digital markets for decades and is steady developing as evident by new emerging gaming services such as gaming video streaming, online gaming, and cloud gaming. While the market is rapidly growing, the quality of these services depends strongly on network characteristics as well as resource management. With the advancement of encoding technologies such as hardware accelerated engines, fast encoding is possible for delay sensitive applications such as cloud gaming. Therefore, already existing video quality models do not offer a good performance for cloud gaming applications. Thus, in this paper, we provide a gaming video quality dataset that considers hardware accelerated engines for video compression using the H.264 standard. In addition, we investigate the performance of signal-based and parametric video quality models on the new gaming video dataset. Finally, we build two novel parametric-based models, a planning and a monitoring model, for gaming quality estimation. Both models are based on perceptual video quality dimensions and can be used to optimize the resource allocation of gaming video streaming services.