SeaAlert: Critical Information Extraction From Maritime Distress Communications with Large Language Models
Researchers' LLM framework tackles noisy VHF radio messages where standard ASR fails, using synthetic data.
A team of researchers including Tomer Atia, Yehudit Aperstein, and Alexander Apartsin has introduced SeaAlert, a novel framework that uses large language models (LLMs) to extract critical safety information from maritime distress communications transmitted over VHF radio. These messages, governed by the Global Maritime Distress and Safety System (GMDSS), are notoriously difficult for automated systems to analyze due to background noise, speaker stress, deviations from standard formats, and errors introduced by automatic speech recognition (ASR). SeaAlert's core innovation is its ability to robustly parse these imperfect transcripts to identify essential details like vessel identity, precise position, and the nature of the emergency.
To overcome the scarcity of real-world labeled data, the researchers built a sophisticated synthetic data generation pipeline. An LLM is first used to produce diverse and realistic textual maritime messages, including challenging variations where standard distress phrases are omitted or replaced with colloquial language. These text samples are then converted to speech, degraded with simulated VHF channel noise to mimic real transmission conditions, and finally run through an ASR system to create the noisy, error-prone transcripts used for training. This method creates a large, controlled dataset that teaches the SeaAlert model to be resilient to the imperfections found in actual emergency broadcasts.
The paper, published on arXiv, demonstrates that this approach allows SeaAlert to function where traditional methods fail. By leveraging the contextual understanding and reasoning capabilities of modern LLMs, the framework can infer missing information and correct ASR errors, potentially automating a critical step in maritime emergency response. This work represents a significant application of AI for operational safety, showing how synthetic data generation can unlock LLM capabilities in domains where high-quality training data is otherwise unavailable.
- Uses LLMs to parse noisy, non-standard maritime distress calls from VHF radio where ASR alone fails.
- Employs a synthetic data pipeline where an LLM generates text, which is then voiced, noised, and transcribed to train the model.
- Aims to automatically extract vessel ID, position, and emergency type to accelerate and improve maritime rescue coordination.
Why It Matters
Automates analysis of chaotic emergency broadcasts, potentially speeding up rescue response times and saving lives at sea.