no code implementations • LREC 2022 • Irina Stenger, Philip Georgis, Tania Avgustinova, Bernd Möbius, Dietrich Klakow
We focus on the syntactic variation and measure syntactic distances between nine Slavic languages (Belarusian, Bulgarian, Croatian, Czech, Polish, Slovak, Slovene, Russian, and Ukrainian) using symmetric measures of insertion, deletion and movement of syntactic units in the parallel sentences of the fable “The North Wind and the Sun”.
no code implementations • RANLP 2021 • Marius Mosbach, Irina Stenger, Tania Avgustinova, Bernd Möbius, Dietrich Klakow
We present an extended version of a tool developed for calculating linguistic distances and asymmetries in auditory perception of closely related languages.
1 code implementation • 4 Jun 2023 • Badr M. Abdullah, Mohammed Maqsood Shaik, Bernd Möbius, Dietrich Klakow
Self-supervised representation learning for speech often involves a quantization step that transforms the acoustic input into discrete units.
1 code implementation • 14 Sep 2022 • Badr M. Abdullah, Bernd Möbius, Dietrich Klakow
Models of acoustic word embeddings (AWEs) learn to map variable-length spoken word segments onto fixed-dimensionality vector representations such that different acoustic exemplars of the same word are projected nearby in the embedding space.
1 code implementation • EMNLP (BlackboxNLP) 2021 • Badr M. Abdullah, Iuliia Zaitova, Tania Avgustinova, Bernd Möbius, Dietrich Klakow
We further discuss the implications of our work on modeling speech processing and language similarity with neural networks.
1 code implementation • 16 Jun 2021 • Badr M. Abdullah, Marius Mosbach, Iuliia Zaitova, Bernd Möbius, Dietrich Klakow
Our experiments show that (1) the distance in the embedding space in the best cases only moderately correlates with phonological distance, and (2) improving the performance on the word discrimination task does not necessarily yield models that better reflect word phonological similarity.
no code implementations • VarDial (COLING) 2020 • Badr M. Abdullah, Jacek Kudera, Tania Avgustinova, Bernd Möbius, Dietrich Klakow
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 and/or non-linguists' perception of language similarity.
1 code implementation • 2 Aug 2020 • Badr M. Abdullah, Tania Avgustinova, Bernd Möbius, Dietrich Klakow
State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between distant languages but also between closely-related languages or even different spoken varieties of the same language.