From Human Speech to Ocean Signals: Transferring Speech Large Models for Underwater Acoustic Target Recognition
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911398966067200 |
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| author | Huang, Mengcheng Zhou, Xue Xu, Chen Man, Dapeng |
| author_facet | Huang, Mengcheng Zhou, Xue Xu, Chen Man, Dapeng |
| contents | Underwater acoustic target recognition (UATR) plays a vital role in marine applications but remains challenging due to limited labeled data and the complexity of ocean environments. This paper explores a central question: can speech large models (SLMs), trained on massive human speech corpora, be effectively transferred to underwater acoustics? To investigate this, we propose UATR-SLM, a simple framework that reuses the speech feature pipeline, adapts the SLM as an acoustic encoder, and adds a lightweight classifier.Experiments on the DeepShip and ShipsEar benchmarks show that UATR-SLM achieves over 99% in-domain accuracy, maintains strong robustness across variable signal lengths, and reaches up to 96.67% accuracy in cross-domain evaluation. These results highlight the strong transferability of SLMs to UATR, establishing a promising paradigm for leveraging speech foundation models in underwater acoustics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18086 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Human Speech to Ocean Signals: Transferring Speech Large Models for Underwater Acoustic Target Recognition Huang, Mengcheng Zhou, Xue Xu, Chen Man, Dapeng Sound Audio and Speech Processing Underwater acoustic target recognition (UATR) plays a vital role in marine applications but remains challenging due to limited labeled data and the complexity of ocean environments. This paper explores a central question: can speech large models (SLMs), trained on massive human speech corpora, be effectively transferred to underwater acoustics? To investigate this, we propose UATR-SLM, a simple framework that reuses the speech feature pipeline, adapts the SLM as an acoustic encoder, and adds a lightweight classifier.Experiments on the DeepShip and ShipsEar benchmarks show that UATR-SLM achieves over 99% in-domain accuracy, maintains strong robustness across variable signal lengths, and reaches up to 96.67% accuracy in cross-domain evaluation. These results highlight the strong transferability of SLMs to UATR, establishing a promising paradigm for leveraging speech foundation models in underwater acoustics. |
| title | From Human Speech to Ocean Signals: Transferring Speech Large Models for Underwater Acoustic Target Recognition |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2601.18086 |