Abstract
Emotionally expressive non-verbal vocalizations can play a major role in human-robot interactions. Humans can assess the intensity and emotional valence of animal vocalizations based on simple acoustic features such as call length and fundamental frequency. These simple encoding rules are suggested to be general across terrestrial vertebrates. To test the degree of this generalizability, our aim was to synthesize a set of artificial sounds by systematically changing the call length and fundamental frequency, and examine how emotional valence and intensity is attributed to them by humans. Based on sine wave sounds, we generated sound samples in seven categories by increasing complexity via incorporating different characteristics of animal vocalizations. We used an online questionnaire to measure the perceived emotional valence and intensity of the sounds in a two-dimensional model of emotions. The results show that sounds with low fundamental frequency and shorter call lengths were considered to have a more positive valence, and samples with high fundamental frequency were rated as more intense across all categories, regardless of the sound complexity. We conclude that applying the basic rules of vocal emotion encoding can be a good starting point for the development of novel non-verbal vocalizations for artificial agents.