## 6 Conclusions and Future Works  
In the \"Talk to Kotaro\" experiment, 37 participants from 10 different regions, speaking a total of 14 languages between them, contributed over 730 audio and video samples of their conversation with a 2D animated screen-based ECA, [PERSON]. In order to investigate how gibberish speech whose phone distribution does not follow a traditional Yulean-like distribution and not a traditional syllabic structure, many different analysis were performed over the audiovisual data recorded in the \"Talk to Kotaro\" web-based crowdsourcing experiment. The research was mostly interested in the immediate emotional changes caused by listening to utterances \(S(w,P)\) with distinct \(w\) vectors of IPA phones and the matrix of the associated acoustic prosody characteristics \(P\), which were chosen accordingly to Algorithm 1. Moreover, we have also analyzed the average emotion displayed by volunteers while listening to [PERSON]'s GS utterances, since it gives a very useful insight of how participants felt during the overall experiment, instead of focusing in their momentary emotional state. Moreover, the quantitative and qualitative investigation performed over the optional Likert scale experiment helped us understand and validate the results of the previous analyses. By analyzing the facial expressions of volunteers in the video samples and the main features of their speech through the MFCC of the audio samples, we were able to verify the findings of [27] that gibberish speech is not very engaging for talking with conversational agents. The experiments yielded little to no positive emotional impact, as indicated by the negative average emotion scores. While the diversity of valence responses suggests sporadic positive experiences, the prevailing estimated sentiment among participants was of impatience and frustration. The difficulty of changing the emotional state of participants and the mostly neutral and negative stance towards the prompts of the Likert scale questionnaire further reinforces the notion that engagement and overall experience provided by gibberish speech in a conversational setting were sub-optimal. The study's alignment with previous research underscores the challenge of forging engaging interactions with GS-speaking agents for adults, which suggests that it is not a recommended means of communication for a conversational setting, since conversations tend to feel one-sided, as highlighted by the attitude towards prompt \(P_{6}\). Delving deeper into the analysis of prosody, interaction duration, and phone choice, attempts to understand what characteristics of the GS utterances generated the estimated impressions, the results of Sections 4.2 and 5 show that the correlation between the prosodic parameters and the immediate emotion change on volunteers were not statistically relevant, barring a very weak correlation between pitch and arousal. Divergent impression patterns among participants from different cultural backgrounds (Japanese and Brazilian) suggest that the initial hypothesis of a cross-cultural preference for specific prosodic attributes does not hold, underscoring the complexity of cross-cultural communication preferences. However, further investigation on the effect of sample size on calculated \(p\)-values show that much more data are necessary to strengthen the finding of our work, which will be achieved through a longer user study. The trained \(GRU_{phones}\) neural networks used for predicting valence and arousal changes from the tokenized IPA phones of the generated GS utterances achieved good performance for training and validation data; however, its generalization capabilities were lackluster. The interest in the pre-trained \(GRU_{phones}\) models lies in their learned embedding hyperspaces, particularly where each phone is positioned relative to other phones.