AI-Enhanced Speech Processing Communication in Wind Energy Feedback Systems

The rapid expansion of wind energy technologies has increased the demand for practical, lab-based training that prepares students and professionals with technical and communication skills essential for safe and efficient operations. In wind energy Feedback system labs, effective instructor–trainee communication is critical, particularly when dealing with complex procedures such as turbine maintenance, performance monitoring, and safety protocols. However, conventional communication methods are often hindered by noisy lab environments, technical jargon misunderstandings, and limited real-time feedback, resulting in reduced clarity and learning efficiency. To address this issue, the paper propose the AI-Enhanced Speech Processing for Wind Energy Feedback system (AISP-WEE) framework, which integrates speech recognition, noise reduction, domain-specific language modeling, and real-time feedback mechanisms to improve clarity and interaction. AISP-WEE employs advanced natural language processing and acoustic modeling to filter ambient noise, transcribe domain-specific instructions, and provide adaptive feedback through bilingual captions, alerts, and interactive prompts. Experimental evaluation in simulated wind lab environments demonstrated that AISP-WEE improved speech recognition accuracy by 28%, reduced communication errors by 33%, and enhanced trainee task performance by 24% compared to traditional verbal instruction methods. These results highlight the potential of AI-driven speech processing systems in improving both the effectiveness and accessibility of wind energy Feedback system. In conclusion, AISP-WEE offers a scalable, intelligent solution that enhances instructor–trainee communication, ensures safety, and fosters more effective experiential learning in renewable energy training environments.

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