Vibration sensor based on the optical fiber ring resonator for intelligent speech recognition empowered by AI
As the primary medium of human communication, speech demands high-performance acquisition and analysis. Conventional sensors often lack sufficient sensitivity, signal-to-noise ratio, and immunity to electromagnetic interference, limiting the capture of subtle acoustic features. To address this, we propose a Fiber Ring Resonator (FRR) as the sensing element for speech-induced vibrations. Leveraging its high quality factor and strong field enhancement, the FRR significantly improves sensitivity. By integrating the Pound–Drever–Hall (PDH) technique, the system locks onto and demodulates minute sound-pressure-induced frequency shifts, enabling robust extraction of multi-frequency signals. Machine learning algorithms then perform deep feature extraction and intelligent classification to suppress background noise and boost recognition accuracy. This fusion offers a promising solution for non-invasive laryngeal diagnosis, secure acoustic monitoring, and intelligent human-computer interaction, paving the way for next-generation speech perception systems.