Intent classification for voice-based military information search on digital maps using integrated BiGRU-CNN network and speech recognition technology

Searching for information is one of the most important functions of software that supports drafting operational documents on digital maps. To enhance usability and meet the demands of modern military operations, it is necessary to automate the information search function using voice commands. A universal voice search tool that supports searches for various types of information requires an initial step of search intent classification. This paper proposes the development of a search intent classification process using an integrated BiGRU-CNN network and automatic speech recognition technology (ASR). The BiGRU-CNN network leverages the advantages of both BiGRU and CNN models to improve the efficiency of classifying text data converted from speech using the Whisper model. The paper compares the proposed method with those that use separate machine learning models combined with feature extraction methods such as TF-IDF, N-gram, and SVD. While the ASR model used in this research still has constraints, experimental results show that the accuracy of search intent classification reaches up to 98.4%. This result is higher than that of compared methods using simpler machine learning models, demonstrating the effectiveness of the proposed method.