Integrating advanced artificial intelligence (AI) and voice recognition into dental devices can improve precision, efficiency, and safety. In endodontics, an offline voice assistant can optimize the performance of devices like the EndoPilot, enhance productivity, reduce contamination risk, and minimize operational errors.

This study describes the development of a fully offline voice assistant system for the EndoPilot device. Key technical challenges addressed include speech recognition in noisy dental clinics, optimization of signal processing algorithms, and energy-efficient edge computing for real-time command processing. Advanced digital signal processing (DSP) techniques for noise reduction, custom speech models, and edge-based audio processing were implemented to achieve high recognition accuracy.

Testing showed a recognition accuracy of 98.2% in quiet environments and 93.6% in real dental clinics, with a response time of 230 milliseconds, enabling real-time device control.

This system represents an innovative approach to contactless control of dental devices and demonstrates the potential of AI-powered solutions for clinical dentistry. Its implementation may enhance workflow efficiency, patient safety, and the adoption of smart dental technologies.

Keywords: Offline Speech Recognition, Endodontics, Digital Signal Processing (DSP), Noise Reduction, Artificial Intelligence, Real-Time Control, Edge Computing.

Mr. Hamzeh Mirzaei is a dedicated professional based in Itzehoe, Germany, currently associated with Sch Company. With strong experience in his field, he contributes to the organization’s operations through his commitment to quality, efficiency, and continuous improvement. Mr. Mirzaei is known for his proactive approach, strong work ethic, and ability to collaborate effectively within diverse teams. His professional journey reflects a focus on responsibility, growth, and delivering reliable results. He remains actively engaged in expanding his expertise and supporting organizational development