Auditory systems are the most efficient and direct strategy for communication between human beings and robots. In this domain, flexible acoustic sensors with magnetic, electric, mechanical, and optic foundations have attracted significant attention as key parts of future voice user interfaces (VUIs) for intuitive human–machine interaction. This study investigated a novel machine learning-based voice recognition platform using an MXene/MoS₂ flexible vibration sensor (FVS) with high sensitivity for acoustic recognition. The performance of the MXene/MoS₂ FVS was systematically investigated both theoretically and experimentally, and the MXene/MoS₂ FVS exhibited high sensitivity (25.8 mV/dB). An MXene/MoS₂ FVS with a broadband response of 40–3,000 Hz was developed by designing a periodically ordered architecture featuring systematic optimization. This study also investigated a machine learning-based speaker recognition process, for which a machine-learning-based artificial neural network was designed and trained. The developed neural network achieved high speaker recognition accuracy (99.1%).

Ammonia (NH₃) detection at room temperature has attracted considerable attention because of the increasing demand for health monitoring, personal safety protection, and industrial manufacturing. Herein, we report the synthesis of polycrystalline SrGe₄O₉ nanotubes (NTs) via an electrospinning process. These NTs are a new sensing material for the detection of ammonia at room temperature. The SrGe₄O₉ NTs exhibited a maximum sensing response of 2.49 for 100 ppm NH₃, which was increased to 7.08 by decorating the NTs with Pt nanoparticles. Flexible gas sensors were fabricated, which exhibited comparable performance to the rigid device. Additionally, the flexible devices showed excellent flexibility, mechanical stability, and sensing stability under different bending states, manifesting their potential applications in flexible and wearable electronics.