Sound masking by a low-pitch speech-shaped noise improves a social robot's talk in noisy environments.

There has been a surge in the use of social robots for providing information, persuasion, and entertainment in noisy public spaces in recent years. Considering the well-documented negative effect of noise on human cognition, masking sounds have been introduced. Masking sounds work, in principle, by making the intrusive background speeches less intelligible, and hence, less distracting. However, this reduced distraction comes with the cost of increasing annoyance and reduced cognitive performance in the users of masking sounds. In a previous study, it was shown that reducing the fundamental frequency of the speech-shaped noise as a masking sound significantly contributes to its being less annoying and more efficient. In this study, the effectiveness of the proposed masking sound was tested on the performance of subjects listening to a lecture given by a social robot in a noisy cocktail party environment. The results indicate that the presence of the masking sound significantly increased speech comprehension, perceived understandability, acoustic satisfaction, and sound privacy of the individuals listening to the robot in an adverse listening condition. To the knowledge of the authors, no previous work has investigated the application of sound masking technology in human-robot interaction designs. The future directions of this trend are discussed.