Playing a Part: Speaker Verification at the Movies
This work addresses the challenge of speaker verification in movies for applications like content analysis, but it is incremental as it primarily benchmarks existing models on new data.
The paper investigates the performance of speaker recognition models on movie speech, where actors disguise their voices, and finds that verification and identification performance drops steeply on the new VoxMovies dataset, with simple domain adaptation methods showing limited improvement.
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.