Linguists should learn to love speech-based deep learning models

2025/12/16 by Marianne de Heer Kloots, Paul Boersma, Kloots, Marianne de Heer +3 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Bridging (networking) #Deep learning #Explainable Artificial Intelligence (XAI) #Focus (optics) #Generative grammar #Language acquisition #Multimodal Machine Learning Applications #Topic Modeling #cs.CL #cs.SD #eess.AS #q-bio.NC

paper · pdf · doi:10.48550/arxiv.2512.14506

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/12/16 · openalex created_date 2025/12/18 · openalex updated_date 2026/07/28

Abstract

Futrell and Mahowald present a useful framework bridging technology-oriented deep learning systems and explanation-oriented linguistic theories. Unfortunately, the target article's focus on generative text-based LLMs fundamentally limits fruitful interactions with linguistics, as many interesting questions on human language fall outside what is captured by written text. We argue that audio-based deep learning models can and should play a crucial role.

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Discussions

• 'Tis the season to preprint BBS commentaries; I'm happy to share ours too! 🎄✨ The textual basis of current LLMs causes trouble, but linguistically relevant insights *can* be found in systems modellin [bsky, 28 points, 1 comments]

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