incom.py 2.0 - Calculating Linguistic Distances and Asymmetries in Auditory Perception of Closely Related Languages

Marius Mosbach, Irina Stenger, Tania Avgustinova, Bernd Möbius, Dietrich Klakow

Abstract
We present an extended version of a tool developed for calculating linguistic distances and asymmetries in auditory perception of closely related languages. Along with evaluating the metrics available in the initial version of the tool, we introduce word adaptation entropy as an additional metric of linguistic asymmetry. Potential predictors of speech intelligibility are validated with human performance in spoken cognate recognition experiments for Bulgarian and Russian. Special attention is paid to the possibly different contributions of vowels and consonants in oral intercomprehension. Using incom.py 2.0 it is possible to calculate, visualize, and validate three measurement methods of linguistic distances and asymmetries as well as carrying out regression analyses in speech intelligibility between related languages.
Anthology ID:
2021.ranlp-1.110
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
968–977
Language:
URL:
https://aclanthology.org/2021.ranlp-1.110
DOI:
Bibkey:
Cite (ACL):
Marius Mosbach, Irina Stenger, Tania Avgustinova, Bernd Möbius, and Dietrich Klakow. 2021. incom.py 2.0 - Calculating Linguistic Distances and Asymmetries in Auditory Perception of Closely Related Languages. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 968–977, Held Online. INCOMA Ltd..
Cite (Informal):
incom.py 2.0 - Calculating Linguistic Distances and Asymmetries in Auditory Perception of Closely Related Languages (Mosbach et al., RANLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/add_acl24_videos/2021.ranlp-1.110.pdf
PDF Search