2024 INTERSPEECH INTERSPEECH 2024

Cross-Linguistic Intelligibility of Non-Compositional Expressions in Spoken Context

Abstract

This study investigates intelligibility of non-compositional expressions in spoken context for five closely related Slavic languages (Belarusian, Bulgarian, Czech, Polish, and Ukrainian) by native Russian speakers. Our investigation employs a web-based experiment involving free-response and multiple-choice translation tasks. Drawing on prior research, two factors were examined: (1) linguistic similarities (orthographic and phonological distances), and (2) surprisal scores obtained from two multilingual speech representation (SR) models fine-tuned for Russian (Wav2Vec2-Large-Ru-Golos-With-LM and Whisper Medium Russian). According to the results of Pearson correlation and regression analyses, phonological distance appears to be a better predictor of intelligibility scores than SR surprisal.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🧭 Keyword Pioneer — cross-linguistic intelligibility
🐝 Cross-Pollinator — Artificial Intelligence, Deep Learning, Machine Learning, Natural Language Processing, Speech & Audio