## 6 Conclusions and Future Works
The initial hypothesis to be tested in such analysis is that phones with close articulation location in the human mouth were expected to be close to each other in the valence and arousal embedding spaces, since it was expected that they would generate similar impressions on listeners. Yet, limitations stemming from the stochastic nature of the algorithm while selecting phones for [PERSON]'s utterances did not allow all phones to figure in the data set, and thus reduced the capacity to which deeper investigations can be performed. Nonetheless, through the K-means clustering method and by computing the distances among all phones, we found out that the phones that are the closest to each other in the embedding space, more often than not, do not have a close articulation locus, showing no support for the original hypothesis. However, since not all phones were used in the experiment, Algorithm 1 needs to be modified to take into account phone frequency and which phones have not yet been used during interactions. The results of Section 4.6 help to understand why most of the impressions presented by the research subjects were of low valence and low-to-moderate arousal, which is consistent with the mostly neutral or slightly negative attitude towards the ECA revealed by the analysis of the responses to the Likert scale prompts. It also shows that participants attribute a low level of intelligence to humanoid-like ECAs that only speak gibberish. The turn-based conversation was not a good interface for the research, since the participants did not particularly enjoy it, and for further experiments, VAD should be used to guarantee more natural conversations. The route of predicting human impression from the gibberish speech patterns does not seem promising, as well as its use for human-computer and human-robot conversation, since research subjects seemed to not enjoy the experience, since they could not understand what the robot avatar was trying to convey. Even though research subjects were aware of the fact that the [PERSON] avatar did not speak semantic speech, they seemed to still be trying to understand what meaning it was trying to convey. This way, it is necessary to compare the performance non-Yulean gibberish speech with Yulean gibberish speech and against other semantic-free utterances in order to better understand how it performs against other SFUs. Moreover, it is necessary to compare how those SFUs perform in a conversational vs. expressive setting, where conversational agents use SFUs to make the listeners believe the agent feels a certain way. Regarding prosody selection, the \(GSIP\) system was not able to predict human impression very well for test data, showing a lack of generalization capabilities. Yet, it obtained a better performance than \(MLP_{profile+prosody}\) and \(GRU_{phones}\), showing that taking information about the conversation partner, acoustic prosody capabilities, and the phones of the GS utterance allows the system to make more accurate predictions, even though it shows a strong preference for small emotional change predictions, which is in accordance with most of the data set. It is necessary to take into account that the lack of correlation shown between prosody parameters and the clash between the attitudes towards prompts \(P_{3}\) and \(P_{6}\) for male respondents might hint towards a complicated relationship between phone choice and impression. Another issue that is worth investigating is the validity of using facial expressions to estimate the emotional state of participants in low valence and arousal states, since it might be difficult to distinguish their actual emotion, since it is very close to neutrality. For future experimentation, another possible route to establish rapport and show that the agent understands the emotion behind the words said by users is to use the prosody synchronicity approach [63], where the conversational agent establishes rapport by copying the prosodic parameters of the interlocutor.