# Synthesis of Non-Linguistic Utterances for Sound Design Support Using a Genetic Algorithm
## 4 Discussion
The clear differentiation of these sounds from others underscores the system's effectiveness in creating NLUs that could be used to express positive feedback, success, or greeting, enhancing social robots' ability to engage in positively perceived interactions. * Sad (Cluster 5): The lower valence and arousal levels in this cluster signify sadness. The ability to generate sounds that are significantly recognized as sad by participants points to the system's nuanced understanding of emotional expression, allowing for a broadened communicative capacity in scenarios requiring empathy or consolation. Significant differences were found between clusters representing the emotions of Activated/Alert and Tense/Fearful/Angry, Activated/Alert and Sad, Tense/Fearful/Angry and Neutral/Calm, Tense/Fearful/Angry and Happy/Excited, Neutral/Calm and Sad, and Happy/Excited and Sad. No significant differences were found between the clusters representing the emotions of Activated/Alert and Neutral/Calm, Tense/Fearful/Angry and Sad, and Neutral/Calm and Happy/Excited. Considering these results, the significant differences were found to be between the most different emotions, or the clusters farthest apart in the Valence Arousal space, whereas those closer together or more similar, with overlapping valence and/or arousal, were harder to differentiate. It is also notable that the method was able to differentiate between Neutral/Calm and Sad, emotions that are closer together on the valence arousal plane and would normally be easily confused. This suggests that the method can also effectively navigate the subtle nuances between a lack of strong emotional expression and the expression of negative emotions. This method was able to differentiate and represent specific emotions effectively, which is crucial for designing social robots that can express a wide range of emotions, from alertness or excitement to sadness, thereby enhancing their ability to engage with humans in a more meaningful and contextually appropriate manner. The findings indicate that, while the system effectively covers a wide area of the valence arousal space, particularly emotions associated with tension, fear, happiness, and excitement, it also reveals areas for improvement in capturing the full range and nuance of human emotions. This could be achieved with more than just the 80 sounds used in this analysis. The experiment's results, revealing the challenges participants faced in accurately interpreting the valence of sounds, point to the complexity of designing sounds that convey intended emotional cues clearly. The success in generating sounds that span a significant portion of the emotional spectrum, however, supports the utility of our approach for creating more nuanced and expressive interactions between robots and humans. The current work has used a GA to synthesize NLUs, starting from a genotype specification designed to correspond to existing NLUs, but allowing the algorithm to develop NLUs starting from random samples and exploring the available sample space. The sounds were validated using a random forest regressor trained on extracted NLUs [15]. [PERSON] et al. [1] sampled sounds from R2D2 and WallE, while [PERSON] et al. [5] designed sounds with the help of professional sound designers and musicians. [PERSON] et al. [6]used similar methods to [PERSON] et al. [5] to create random NLUs. [PERSON] [3] created an automatic NLU generation system using a neural network. These all used categorical labels including intentions, emotions, and dialogue parts, whereas the current work, as well as parts of [PERSON]'s Ph.D. work [3], used dimensional measures of affect. In all the mentioned research, pitch, timbre, amplitude, and time were varied to create NLUs.