mlsanityData to 7 Oct 2026

Tonguescape: Exploring Language Models Understanding of Vowel Articulation

arXiv 2501.17643 ↗First posted 29 Jan 2025cs.CL cs.AIcited by 1

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Abstract (arXiv)

Vowels are primarily characterized by tongue position. Humans have discovered these features of vowel articulation through their own experience and explicit objective observation such as using MRI. With this knowledge and our experience, we can explain and understand the relationship between tongue positions and vowels, and this knowledge is helpful for language learners to learn pronunciation. Since language models (LMs) are trained on a large amount of data that includes linguistic and medical fields, our preliminary studies indicate that an LM is able to explain the pronunciation mechanisms of vowels. However, it is unclear whether multi-modal LMs, such as vision LMs, align textual information with visual information. One question arises: do LMs associate real tongue positions with vowel articulation? In this study, we created video and image datasets from the existing real-time MRI dataset and investigated whether LMs can understand vowel articulation based on tongue positions using vision-based information. Our findings suggest that LMs exhibit potential for understanding vowels and tongue positions when reference examples are provided while they have difficulties without them. Our code for dataset building is available on GitHub.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 31 references that are in the map. First posted on arXiv 29 Jan 2025.

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14 of 31 references.

References · 14

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  2. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
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  11. 2017Probabilistic Typology: Deep Generative Models of Vowel Inventoriescited by 8
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2 more
  1. 2023Information-Theoretic Characterization of Vowel Harmony: A Cross-Linguistic Study on Word Listscited by 4
  2. 2018A Deep Generative Model of Vowel Formant Typologycited by 3

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