Robust LSTM-Based NLP Model for Efficient Classification of Bike and Non-Bike Commands

Le résumé fourni par la source

This paper presents an effective approach for classifying bike-specific commands from non-bike-related inputs in two-wheeler systems using Natural Language Processing (NLP) techniques. The proposed method leverages a Long Short-Term Memory (LSTM) model to accurately distinguish between commands that are relevant to bike functionality and unrelated voice inputs. By focusing on real-time voice interactions in dynamic environments, this model enhances user experience by ensuring quick and precise command recognition, crucial for voice-assisted operations in modern two-wheelers. Extensive experiments demonstrate the model's efficiency, showcasing significant improvements in accuracy and responsiveness compared to traditional classification techniques.

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