Playing a part: speaker verification at the movies

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...

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Main Authors: Brown, A, Huh, J, Nagrani, A, Chung, JS
Format: Conference item
Language:English
Published: IEEE 2021
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author Brown, A
Huh, J
Nagrani, A
Chung, JS
author_facet Brown, A
Huh, J
Nagrani, A
Chung, JS
author_sort Brown, A
collection OXFORD
description 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.
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spelling oxford-uuid:1f57f566-84db-449c-ab7b-84fce34ae6112022-03-26T11:21:14ZPlaying a part: speaker verification at the moviesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:1f57f566-84db-449c-ab7b-84fce34ae611EnglishSymplectic ElementsIEEE2021Brown, AHuh, JNagrani, AChung, JSThe 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.
spellingShingle Brown, A
Huh, J
Nagrani, A
Chung, JS
Playing a part: speaker verification at the movies
title Playing a part: speaker verification at the movies
title_full Playing a part: speaker verification at the movies
title_fullStr Playing a part: speaker verification at the movies
title_full_unstemmed Playing a part: speaker verification at the movies
title_short Playing a part: speaker verification at the movies
title_sort playing a part speaker verification at the movies
work_keys_str_mv AT browna playingapartspeakerverificationatthemovies
AT huhj playingapartspeakerverificationatthemovies
AT nagrania playingapartspeakerverificationatthemovies
AT chungjs playingapartspeakerverificationatthemovies