Linguists should learn to love speech-based deep learning models
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909965458866176 |
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| author | Kloots, Marianne de Heer Boersma, Paul Zuidema, Willem |
| author_facet | Kloots, Marianne de Heer Boersma, Paul Zuidema, Willem |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_14506 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Linguists should learn to love speech-based deep learning models Kloots, Marianne de Heer Boersma, Paul Zuidema, Willem Computation and Language Sound Audio and Speech Processing Neurons and Cognition 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. |
| title | Linguists should learn to love speech-based deep learning models |
| topic | Computation and Language Sound Audio and Speech Processing Neurons and Cognition |
| url | https://arxiv.org/abs/2512.14506 |