Evaluating automatic speech recognition systems as quantitative models of cross-lingual phonetic category perception

被引：4

作者：

Schatz, Thomas ^{[1
,2
]}

Bach, Francis ^{[3
]}

Dupoux, Emmanuel ^{[4
]}

机构：

[1] Univ Maryland, Dept Linguist, College Pk, MD 20742 USA

[2] Univ Maryland, UMIACS, College Pk, MD 20742 USA

[3] PSL Res Univ, CNRS, Ecole Normale Super, Dept Informat ENS,SIERRA Project Team,INRIA, 45 Rue Ulm, F-75005 Paris, France

[4] PSL Res Univ, CNRS, Ecole Normale Super, Dept Etud Cognit ENS,EHESS,LSCP, 29 Rue Ulm, F-75005 Paris, France

来源：

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA | 2018年 / 143卷 / 05期

基金：

欧洲研究理事会; 美国国家科学基金会;

关键词：

JAPANESE;

D O I：

10.1121/1.5037615

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Theories of cross-linguistic phonetic category perception posit that listeners perceive foreign sounds by mapping them onto their native phonetic categories, but, until now, no way to effectively implement this mapping has been proposed. In this paper, Automatic Speech Recognition systems trained on continuous speech corpora are used to provide a fully specified mapping between foreign sounds and native categories. The authors show how the machine ABX evaluation method can be used to compare predictions from the resulting quantitative models with empirically attested effects in human cross-linguistic phonetic category perception. (C) 2018 Acoustical Society of America

引用

页码：EL372 / EL378

页数：7

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