AI-Based Speech Recognition Systems for EV Technician Education in Charging Infrastructure Operations
Annotatsiya
The rapid expansion of electric vehicle (EV) adoption has created a pressing need for skilled technicians capable of managing and maintaining complex charging infrastructure systems. Traditional training methods for EV technicians often rely on textual manuals, in-person demonstrations, and unstructured learning materials, which limit the ability of learners to efficiently acquire operational knowledge and quickly respond to technical issues. To address these challenges, this paper proposes an AI-Based Speech Recognition System for EV Technician Education in Charging Infrastructure Operations (AI-SR-TEC), which leverages advanced deep learning and natural language processing techniques to transcribe, interpret, and provide interactive feedback on spoken instructions and technician queries. AI-SR-TEC integrates automatic speech recognition, domain-specific terminology adaptation, and real-time corrective guidance to enhance the training experience, enabling technicians to practice hands-on procedures with voice-guided support. Experimental evaluation on a curated dataset of EV charging operation dialogues demonstrated that AI-SR-TEC achieved a word recognition accuracy of 94%, improved task completion efficiency by 31%, and enhanced learner comprehension scores by 28% compared to conventional lecture-based training approaches. The system also facilitated adaptive learning by identifying recurring errors and providing targeted feedback. In conclusion, AI-SR-TEC offers a scalable and intelligent solution for advancing EV technician education, bridging the gap between theoretical instruction and practical operational proficiency, and promoting efficient skill acquisition in EV charging infrastructure management.