Campus Units

English

Document Type

Conference Proceeding

Publication Version

Published Version

Publication Date

2020

Journal or Book Title

Proceedings of Interspeech 2020

First Page

1763

Last Page

1767

DOI

10.21437/Interspeech.2020-2910

Abstract

This paper presents a methodology to study the role of nonnative accents on talker recognition by humans. The methodology combines a state-of-the-art accent-conversion system to resynthesize the voice of a speaker with a different accent of her/his own, and a protocol for perceptual listening tests to measure the relative contribution of accent and voice quality on speaker similarity. Using a corpus of non-native and native speakers, we generated accent conversions in two different directions: non-native speakers with native accents, and native speakers with non-native accents. Then, we asked listeners to rate the similarity between 50 pairs of real or synthesized speakers. Using a linear mixed effects model, we find that (for our corpus) the effect of voice quality is five times as large as that of non-native accent, and that the effect goes away when speakers share the same (native) accent. We discuss the potential significance of this work in earwitness identification and sociophonetics.

Comments

This proceeding is published as Das, Anurag, Guanlong Zhao, John Levis, and Evgeny Chukharev-Hudilainen. "Understanding the Effect of Voice Quality and Accent on Talker Similarity." Proceedings of Interspeech 2020. [Virtual Conference, October 25-29, 2020.] Pages 1763-1767. DOI: 10.21437/Interspeech.2020-2910. Posted with permission.

Copyright Owner

ISCA

Language

en

File Format

application/pdf

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