用语音大模型识别水下目标，效果超99%。
From Human Speech to Ocean Signals: Transferring Speech Large Models for Underwater Acoustic Target Recognition
- 复用语音特征提取流程，用语音大模型做声学编码器
- 在真实数据集上达99%准确率，跨域测试仍超96.67%
- 适合缺乏标注数据的水下声学任务研究者
水下声学目标识别（UATR）在海洋应用中至关重要，但受限于标注数据少和海洋环境复杂，仍具挑战。本文探讨核心问题：训练于海量语音数据的语音大模型（SLMs）能否有效迁移至水下声学任务？为此，提出UATR-SLM框架，复用语音特征处理流程，将SLM作为声学编码器，并添加轻量分类器。在DeepShip和ShipsEar基准测试中，UATR-SLM实现超过99%的域内准确率，对不同信号长度保持强鲁棒性，跨域评估最高达96.67%。结果表明SLMs在UATR中具备强大可迁移性，为利用语音基础模型解决水下声学问题提供了新范式。
原文摘要 · Abstract (English)
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.
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