  A text-independent speaker recognition system relies on successfully encoding
speech factors such as vocal pitch, intensity, and timbre to achieve good
performance. A majority of such systems are trained and evaluated using spoken
voice or everyday conversational voice data. Spoken voice, however, exhibits a
limited range of possible speaker dynamics, thus constraining the utility of
the derived speaker recognition models. Singing voice, on the other hand,
covers a broader range of vocal and ambient factors and can, therefore, be used
to evaluate the robustness of a speaker recognition system. However, a majority
of existing speaker recognition datasets only focus on the spoken voice. In
comparison, there is a significant shortage of labeled singing voice data
suitable for speaker recognition research. To address this issue, we assemble
\textit{JukeBox} - a speaker recognition dataset with multilingual singing
voice audio annotated with singer identity, gender, and language labels. We use
the current state-of-the-art methods to demonstrate the difficulty of
performing speaker recognition on singing voice using models trained on spoken
voice alone. We also evaluate the effect of gender and language on speaker
recognition performance, both in spoken and singing voice data. The complete
\textit{JukeBox} dataset can be accessed at
http://iprobe.cse.msu.edu/datasets/jukebox.html.
