Automatic Emergency Detection in Naval VHF Transmissions

Abstract

Abstract As the proficiency of Speech-To-Text (STT) services increases, so does the possible applications. This thesis explores the use of such services in a very special domain, naval VHF transmissions. It explores STT service performance and details the development of a domain-specific Speech-To-Text model based on the self-supervised wav2vec 2.0 architecture. This enabled the recognition of emergency messages using keyword detection and also created a foundation for more advanced intent analysis in the future. The developed model outperforms Google on the naval domain and achieves good classification results using keyword detection, managing to discern most messages containing one or more keywords. This performance meant that the model could be used as an aid for actual emergency message detection by Sjöfartsverket. The research also shows that many of the pre-trained models do not have adequate performance on the intended domain, but it was noted that using semi-supervised methods such pre-trained models can be tuned to reach acceptable performance levels. This can be done with smaller sets of domain-specific data to achieve good results on the specific domains without the need for a completely new model for each domain.