@inproceedings{abdullah-etal-2020-rediscovering,
title = "Rediscovering the {S}lavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification",
author = {Abdullah, Badr M. and
Kudera, Jacek and
Avgustinova, Tania and
M{\"o}bius, Bernd and
Klakow, Dietrich},
editor = {Zampieri, Marcos and
Nakov, Preslav and
Ljube{\v{s}}i{\'c}, Nikola and
Tiedemann, J{\"o}rg and
Scherrer, Yves},
booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics (ICCL)",
url = "https://aclanthology.org/2020.vardial-1.12",
pages = "128--139",
abstract = "Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification (LID). In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness or non-linguists{'} perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability to be the best predictor of the language representation similarity.",
}
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<abstract>Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification (LID). In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness or non-linguists’ perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability to be the best predictor of the language representation similarity.</abstract>
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%0 Conference Proceedings
%T Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification
%A Abdullah, Badr M.
%A Kudera, Jacek
%A Avgustinova, Tania
%A Möbius, Bernd
%A Klakow, Dietrich
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Scherrer, Yves
%S Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2020
%8 December
%I International Committee on Computational Linguistics (ICCL)
%C Barcelona, Spain (Online)
%F abdullah-etal-2020-rediscovering
%X Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification (LID). In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness or non-linguists’ perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability to be the best predictor of the language representation similarity.
%U https://aclanthology.org/2020.vardial-1.12
%P 128-139
Markdown (Informal)
[Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification](https://aclanthology.org/2020.vardial-1.12) (Abdullah et al., VarDial 2020)
ACL