Machine Learning-Based Intent Analysis System Using Voice Recognition for Imdported Food Information Delivery

Recently, machine learning is increasingly applied in the food industry and related research. Especially, imported food access to up-to-date information can be challenging for consumers. To address this issue, we propose a machine learning-based intent analysis method for imported food information using speech recognition to improve the current situation. Checking for the presence of predicates within the sentence data transformed into a text format and if they are detected, we apply filtering to improve detection accuracy. Through the proposed method, the capability to accurately designate product names and convey information is demonstrated even in cases where voice data is misinterpreted. Unsuitable and recalled product information for specific imported food items can be effectively conveyed. By delivering accurate information related to imported food products to consumers, we can assist in promoting a healthy eating culture, enabling individuals to make informed choices.