# Synthesis of Non-Linguistic Utterances for Sound Design Support Using a Genetic Algorithm
###### Abstract  
As social robots become more prevalent, they often employ non-speech sounds, in addition to other modes of communication, to communicate emotion and intention in an increasingly complex visual and audio environment. These non-speech sounds are usually tailor-made, and research into the generation of non-speech sounds that can convey emotions has been limited. To enable social robots to use a large amount of non-speech sounds in a natural and dynamic way, while expressing a wide range of emotions effectively, this work proposes an automatic method of sound generation using a genetic algorithm, coupled with a random forest model trained on representative non-speech sounds to validate each produced sound's ability to express emotion. The sounds were tested in an experiment wherein subjects rated the perceived valence and arousal. Statistically significant clusters of sounds in the valence arousal space corresponded to different emotions, showing that the proposed method generates sounds that can readily be used in social robots.  
genetic algorithms; human-robot interaction; sound design; non-linguistic utterances; machine learning +
Footnote †: journal:  
communication channels including gestures, actions, expressions, movements, colors, and normal speech.  
This work presents a method of generating NLUs that is different from previous work in the area. Researchers [1; 3; 5; 6] have used varying methods to create NLUs, sometimes in consultation with professional sound designers [7], and some using musical notation schemes or similar abstractions [1; 5]. To ensure that social robots can dynamically generate sounds in any given interaction scenario, a new type of method is needed that does away with manual sound creation in favor of a systematic method of sound generation. This work presents a scheme that uses a MIDI note framework and a genetic algorithm (GA) to generate sounds for use by social robots. The method generates many sounds very rapidly. Experiments proved that people perceived sounds as being able to express emotion. This method of sound generation could be implemented in a system that operates in a social robot that actively and dynamically interacts with users in the real world. This system can create many sounds for each type of emotion, as opposed to previous work in the area which usually only proposed a few sounds for each emotion. In some applications, for example, elderly assistance or alarms, we may want a system that uses only a few different sounds that can be easily learned by users, but in others, where more natural and human-like behavior is desired from the social robot, it would be better to have a system that is capable of producing a wide range of sounds in a dynamic and context-sensitive way. Human-like behavior in robots interacting with humans has been found to improve the perception of competence and warmth of the robots [8]. In this work, we automatically generate many sounds and validate their ability to communicate distinct emotions, through machine learning methods, experiments with human subjects, and statistical analyses.  
While GAs have been used together with a multi-layer perceptron to produce music, going as far back as 1995 [9], the current study concerns non-speech sounds, and the application of that method, together with other mentioned machine learning methods to validate the emotional meaning of the sounds, for use in social robots has not been applied before.