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Found 8 papers, 5 papers with code
Modeling the Impact of Syntactic Distance and Surprisal on Cross-Slavic Text Comprehension
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”.
incom.py 2.0 - Calculating Linguistic Distances and Asymmetries in Auditory Perception of Closely Related Languages
We present an extended version of a tool developed for calculating linguistic distances and asymmetries in auditory perception of closely related languages.
An Information-Theoretic Analysis of Self-supervised Discrete Representations of Speech
Self-supervised representation learning for speech often involves a quantization step that transforms the acoustic input into discrete units.
Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings
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.
How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings
We further discuss the implications of our work on modeling speech processing and language similarity with neural networks.
Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study
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.
Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification
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.
Cross-Domain Adaptation of Spoken Language Identification for Related Languages: The Curious Case of Slavic Languages
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.
