  This paper introduces CocoNut-Humoresque, an open-source large-scale speech
likability corpus that includes speech segments and their per-listener
likability scores. Evaluating voice likability is essential to designing
preferable voices for speech systems, such as dialogue or announcement systems.
In this study, we let 885 listeners rate 1800 speech segments of a wide range
of speakers regarding their likability. When constructing the corpus, we also
collected the multiple speaker attributes: genders, ages, and favorite YouTube
videos. Therefore, the corpus enables the large-scale statistical analysis of
voice likability regarding both speaker and listener factors. This paper
describes the construction methodology and preliminary data analysis to reveal
the gender and age biases in voice likability. In addition, the relationship
between the likability and two acoustic features, the fundamental frequencies
and the x-vectors of given utterances, is also investigated.
