Wearable sound detectors require strain sensors that are stretchable, sensitive, and capable of adhering conformably to the skin, and toward this end, 2D materials hold great promise. However, the vibration of vocal cords and muscle contraction are complex and changeable, which can compromise the sensing performance of devices. By combining deep learning and 2D MXenes, an MXene‐based sound detector is prepared successfully with improved recognition and sensitive response to pressure and vibration, which facilitate the production of a high‐recognition and resolution sound detector. By training and testing the deep learning network model with large amounts of data obtained by the MXene‐based sound detector, the long vowels and short vowels of human pronunciation are successfully recognized. The proposed scheme accelerates the application of artificial throat devices in biomedical fields and opens up practical applications in voice control, motion monitoring, and many other fields.

Flexible acoustic sensors with high sensitivity, excellent mechanical strength, and easy integration are urgently needed for wearable electronics. MXene holds great promise as a sensing material for this application. However, low flexibility and stability limit the performance of MXene‐based composites. To alleviate the aforementioned issue, a flexible pressure sensor based on MXene/poly(3,4‐ethylenediox‐ythiophene)‐poly(styrenesulfonate) (PEDOT:PSS) is fabricated and used as an acoustic sensor inhibiting high sensitivity, fast response time (57 ms), ultra‐thin thickness (30 μm), and remarkable stability. Excellent performance enables the sensor to detect and identify weak muscle movements and skin vibrations, such as word pronunciation and carotid artery pulse. Furthermore, by combining the proposed deep learning model based on number recognition convolutional neural network (NR‐CNN), speech recognition toward different pronunciations of numbers that appear frequently in daily conversations can be realized. High recognition accuracy (91%) is achieved by training and testing the proposed NR‐CNN with large amounts of data recorded by the sensor. Results demonstrate that the flexible and wearable MXene/PEDOT:PSS acoustic sensor accelerates intelligent artificial acoustics and possesses great potential for applications involving speech recognition and health monitoring.