Intentional voice modifications by electronic or nonelectronic means challenge automatic speaker recognition systems. Previous work focused on detecting the act of disguise or identifying everyday speakers disguising their voices. Here, we propose a benchmark for the study of voice disguise, by studying the voice variability of professional voice actors. A dataset of 114 actors playing 647 characters is created. It contains 19 hours of captured speech, divided into 29,733 utterances tagged by character and actor names, which is then further sampled. Text-independent speaker identification of the actors based on a novel benchmark training on a subset of the characters they play, while testing on new unseen characters, shows an EER of 17.1%, HTER of 15.9%, and rank-1 recognition rate of 63.5% per utterance when training a Convolutional Neural Network on spectrograms generated from the utterances. An I-Vector based system was trained and tested on the same data, resulting in 39.7% EER, 39.4% HTER, and rank-1 recognition rate of 13.6%.