Speaker discrimination performance for “easy” versus “hard” voices in style-matched and -mismatched speech

Author:

Afshan Amber1,Kreiman Jody2,Alwan Abeer1

Affiliation:

1. Department of Electrical and Computer Engineering, University of California, Los Angeles, California 90095-1594, USA

2. Departments of Head and Neck Surgery and Linguistics, University of California, Los Angeles, California 90095-1794, USA

Abstract

This study compares human speaker discrimination performance for read speech versus casual conversations and explores differences between unfamiliar voices that are “easy” versus “hard” to “tell together” versus “tell apart.” Thirty listeners were asked whether pairs of short style-matched or -mismatched, text-independent utterances represented the same or different speakers. Listeners performed better when stimuli were style-matched, particularly in read speech−read speech trials (equal error rate, EER, of 6.96% versus 15.12% in conversation–conversation trials). In contrast, the EER was 20.68% for the style-mismatched condition. When styles were matched, listeners' confidence was higher when speakers were the same versus different; however, style variation caused decreases in listeners' confidence for the “same speaker” trials, suggesting a higher dependency of this task on within-speaker variability. The speakers who were “easy” or “hard” to “tell together” were not the same as those who were “easy” or “hard” to “tell apart.” Analysis of speaker acoustic spaces suggested that the difference observed in human approaches to “same speaker” and “different speaker” tasks depends primarily on listeners' different perceptual strategies when dealing with within- versus between-speaker acoustic variability.

Funder

national science foundation

National Institutes of Health

Publisher

Acoustical Society of America (ASA)

Subject

Acoustics and Ultrasonics,Arts and Humanities (miscellaneous)

Reference41 articles.

1. Idenfication of voices in shouting

2. Brümmer, N. (2010). “ Measuring, refining and calibrating speaker and language information extracted from speech,” Ph.D. thesis, University of Stellenbosch, South Africa.

3. Brümmer, N. , and De Villiers, E. (2011b). “ The BOSARIS toolkit user guide: Theory, algorithms and code for binary classifier score processing,” documentation of BOSARIS toolkit 24, https://sites.google.com/site/nikobrummer/ by Agnitio Labs (Last viewed July 24, 2021).

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3