  The goal of this work is to investigate the performance of popular speaker
recognition models on speech segments from movies, where often actors
intentionally disguise their voice to play a character. We make the following
three contributions: (i) We collect a novel, challenging speaker recognition
dataset called VoxMovies, with speech for 856 identities from almost 4000 movie
clips. VoxMovies contains utterances with varying emotion, accents and
background noise, and therefore comprises an entirely different domain to the
interview-style, emotionally calm utterances in current speaker recognition
datasets such as VoxCeleb; (ii) We provide a number of domain adaptation
evaluation sets, and benchmark the performance of state-of-the-art speaker
recognition models on these evaluation pairs. We demonstrate that both speaker
verification and identification performance drops steeply on this new data,
showing the challenge in transferring models across domains; and finally (iii)
We show that simple domain adaptation paradigms improve performance, but there
is still large room for improvement.
