SeaAlert: Critical Information Extraction From Maritime Distress Communications with Large Language Models

TL;DR

SeaAlert leverages large language models to improve the extraction of critical information from noisy, stressed, and variably formatted maritime distress communications, enhancing safety at sea.

Contribution

The paper introduces SeaAlert, a novel LLM-based framework with a synthetic data pipeline to robustly analyze maritime distress messages under challenging conditions.

Findings

Synthetic data generation improves model robustness.

LLM-based analysis handles noisy and variably formatted messages.

Framework enhances safety-critical communication understanding.

Abstract

Maritime distress communications transmitted over very high frequency (VHF) radio are safety-critical voice messages used to report emergencies at sea. Under the Global Maritime Distress and Safety System (GMDSS), such messages follow standardized procedures and are expected to convey essential details, including vessel identity, position, nature of the distress, and required assistance. In practice, however, automatic analysis remains difficult because distress messages are often brief, noisy, and produced under stress, may deviate from the prescribed format, and are further degraded by automatic speech recognition (ASR) errors caused by channel noise and speaker stress. This paper presents SeaAlert, an LLM-based framework for robust analysis of maritime distress communications. To address the scarcity of labeled real-world data, we develop a synthetic data generation pipeline in which an LLM produces realistic and diverse maritime messages, including challenging variants in which standard distress codewords are omitted or replaced with less explicit expressions. The generated utterances are synthesized into speech, degraded with simulated VHF noise, and transcribed by an ASR system to obtain realistic noisy transcripts.