## 5 Discussion
### Effects of Prosody, Duration of Interaction, and Phone Choice  
Previous analysis performed on video, audio, and Likert scale questionnaire answers can help us understand how research subjects felt towards each utterance and the experiment itself, but does nothing to elucidate why, which was one of the goals of the \"Talk to Kotaro\" experiment. In order to understand how acoustic prosody parameters affect the impression of volunteers, we have calculated pair-wise [PERSON]'s \(\tau_{C}\) correlation between each one of the investigated prosodic parameters and valence and arousal changes. Unlike what was previously thought by the researchers, no meaningful correlations could be obtained, with the exception of a very weak correlation of 0.06 between pitch and arousal for all participants, which had a \(p\)-value of 0.03. By performing MANOVA analysis, it was possible to verify that Brazilian research subjects had distinct impression patterns compared to Japanese research subjects. Such analysis was not performed for other nationalities since they had too few participants (fewer than four), and thus, it would be a meaningless comparison between an individual and a group of participants, for most nationalities. However, joining the fact that there were no favoured prosody patterns by all volunteers considered as a single group and that volunteers from different cultures had statistically distinct reactions to prosody parameters, we found no support for the original hypothesis from the \"Talk to Kotaro Experiment\" that, like the Kiki-Bouba effect [56], there would be a cross-cultural preference for certain prosody characteristics; quite the opposite. However, it is necessary to further investigate if the high \(p\)-values are due to the small size of the data set or if there is really no statistically meaningful correlation. One way of performing such analysis is to use for all participants KDE (kernel density estimation) [62] to learn the distribution of the pairs of \((p,r)\), where \(p\) is one of the prosody parameters and \(r\) is the associated \(\delta_{v}\) or \(\delta_{a}\) value. With that, it is possible to create synthetic data whose distribution is very similar to the distribution obtained through the experiment and calculate the correlation between the synthetic set of \(p\) and \(r\) for different quantitiesof synthetic data points until meaningful \(p\)-values are obtained for all pairs of Stuart-Kendall's correlation. Such an analysis is just a ballpark estimate, since it has a very strong assumption: the distribution of the real data obtained through the experiment actually represents (or represents closely enough) the actual distribution of how people react to different prosody parameters in the context of listening to non-Yulean GS. We used a Gaussian kernel and chose a bandwidth of 0.1 to learn the distribution of our data in order to create synthetic data sets. With the distributions learned, we increased the size of the synthetic data sets until we consistently obtained meaningful, albeit still very weak, correlations between the synthetic pairs. We started obtaining mostly relevant correlations by 15,000 data points and always obtained statistically relevant correlations with 20,000 data points. Such a result shows that a much larger data set seems to be necessary in order to allow researchers to make stronger claims regarding the correlation between prosodic parameters and the impression of volunteers. Linear regression was performed on the average emotion of each interaction volunteers had in their experiment sessions as a way of obtaining an overall tendency of how the emotion of participants evolved as they interacted with Kotaro.