# Synthesis of Non-Linguistic Utterances for Sound Design Support Using a Genetic Algorithm
## 5 Conclusions and Future Work  
The current work used a GA to produce NLUs and used a random forest model that was previously trained on NLUs extracted from movies, TV shows, and video games [15] to infer their valence and arousal. An experiment was conducted to compare human ratings of valence and arousal of the GA sounds to the inferred values. Using a GA to create NLUs allows for many sounds to be created very rapidly. Using the previously trained random forest model and human rating experiment, the sounds were validated, and subject interpretations of their valence and arousal and the model inferences corresponded closely. This study successfully combines MIDI-encoded sound generation with machine learning techniques to dynamically produce a broad spectrum of emotionally expressive non-linguistic utterances (NLUs). The capability of this GA-based system to generate a diverse range of sounds that are recognized by humans as indicative of specific emotions highlights its potential for enhancing human-robot interaction. Our method distinguishes itself from previous work by its efficiency in sound production and the application of machine learning for sound validation, offering a new paradigm for sound design in interactive technologies. The proposed method generates a wide variety of ready-to-use sounds for social robots, which represent major emotions well, including Tense/Fearful, Sad, Happy/Excited, Activated/Alert, and Neutral/Calm. Furthermore, the findings of this study, particularly the significant differences and similarities between emotional clusters, offer insights into the method's capacity to navigate the subtleties of emotional expression. The ability to differentiate closely related emotions such as Neutral/Calm and Sad provides evidence of the method's refined understanding of the valence arousal space. This precision in emotional representation opens up new possibilities for creating robots that can more accurately mimic human emotional expressions, enhancing their integration into everyday life. This method and result are unique compared to other work in the field. Most other work used designed sounds or sampled sounds, which could not be produced as rapidly and abundantly. Also, most other work has not used machine learning methods to model and validate sounds. The GA was able to produce NLUs covering an area of the valence and arousal plane corresponding to emotions of tension, fear, happiness, and excitement. A more complex genotype design or other optimization methods could result in sounds that cover a wider range of emotions. When the inferences were compared to subject ratings, MAE values of 0.117 for valence and 0.067 for arousal were obtained, indicating that subject ratings agreed with the model. Subjects had some difficulty assessing the valence of the sounds since the correlation coefficient for valence was low. The GA sounds were comparable to sounds extracted from movies, TV shows, and video games that were designed by experts, and the random forest model can be used to support sound design for NLUs, social robots, machines, or devices that use such sounds. This work has laid the groundwork for a new approach to generating NLUs for social robots, demonstrating the potential of combining genetic algorithms with machine learning and human feedback to create emotionally expressive sounds. The success in generating a wide variety of sounds that can effectively convey a range of emotions marks a significant step forward in the field of human-robot interaction. In future work, the aim is to create a wider variety of sounds that cover more types of emotions. Furthermore, real-life social robot-to-human interaction scenarios should be constructed to test the application of NLUs in a more realistic setting. Larger-scale experiments should be carried out to establish the significance of both valence and arousal in subject interpretations of the generated sounds.