Phonetic Cues in Auditory Identification of Bulgarian, Czech, Polish, and Russian Language of Origin

Author:

Kudera JacekORCID,Stenger Irina,Möbius Bernd,Avgustinova Tania1,Klakow Dietrich2

Affiliation:

1. Department of Language Science and Technology, Saarland University, Germany

2. Department of Language Science and Technology, Saarland University, Germany; Spoken Language Systems (LSV), Saarland University, Germany

Abstract

This work presents the results of an auditory language of origin identification experiment. Disyllabic and trisyllabic logatomes were recorded by speakers of Bulgarian, Czech, Polish, and Russian, and presented to L1 speakers of the abovementioned Slavic languages. The goals of the test were to verify the ability of lay listeners to recognize the linguistic origin of speakers, based on spoken samples with limited segmental and suprasegmental information, and to correlate the signal features with the subjects’ performance. It was found that position of word stress is not an important predictor in language recognition. However, inherent vowel characteristics such as duration and vowel space computed by the means of Pillai scores correlate with subjects’ performance. Both the linguistic profile and the familiarity with closely related languages also appear to be relevant predictors of listeners’ performance. Finally, the information-theoretic notion of surprisal applied on regular cross-linguistic sound correspondences was correlated with recognition scores; though, the correlations did not reach the threshold of statistical significance. We conclude that auditory identification of linguistic origin by lay persons, native speakers of closely related languages, is possible even when exposed to limited segmental information, which can serve as a cue in the identification of linguistic origin.

Funder

Deutsche Forschungsgemeinschaft

Publisher

SAGE Publications

Subject

Speech and Hearing,Linguistics and Language,Sociology and Political Science,Language and Linguistics,General Medicine

Cited by 3 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

1. Research on Grammar Error Correction Method of Russian Literature Writing Based on Improved Neural Network Algorithm;2024 International Conference on Machine Intelligence and Digital Applications;2024-05-30

2. Research on Russian Cultural Transliteration Algorithm Based on Hidden Markov Model;2024 International Conference on Machine Intelligence and Digital Applications;2024-05-30

3. Kako izvorni govornici hrvatskoga jezika razumiju druge slavenske jezike? Istraživanje metodom praćenja pokreta oka;Suvremena lingvistika;2023-12-27

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