A voice search engine for military symbols to enhance the drafting of operational plan documents on digital map

Abstract

The process of searching for information to serve the construction of operational plan documents on a digital map is still being done manually and needs to be automated in order to improve efficiency. Speech recognition and natural language processing technologies, commonly used in chatbots, virtual assistants, voice commands, and voice search, could be promising tools to overcome this problem. This paper proposes a framework for deploying a voice search engine that uses Whisper, a deep learning-based automatic speech recognition model, and combines TF-IDF, N-gram, and Truncated SVD as feature extraction approaches to search for text ground truth in a dictionary of military symbols using Cosine similarity. Despite the small size of a custom dataset, the experiments show promising results, achieving an accuracy of 82.00%. Our achievement surpasses that of several traditional statistical methods and classification models.