可穿戴聲學傳感器通過校準喉部振動并將其轉化為合成語音，為語言障礙者提供了有效的溝通解決方案。本文，香港理工大學Zhongqing SU、南方科技大學周利民教授等在《ACS Sens》期刊發表名為“An Aerosol Jet-Printed Wearable Graphene/Cellulose Nanocrystal Acoustic Sensor for Speech Recognition”的論文，研究開發了一種新型壓阻式聲學傳感器，通過氣溶膠噴射打印技術，采用聚氨酯（PU）薄膜封裝石墨烯/纖維素納米晶體（CNCs）進行增材制造。該傳感器具有高度生物相容性和柔韌性，能夠精確測量變化的聲音壓力水平（SPL）。

實驗結果表明，通過調節石墨烯濃度可調控傳感器的聲學靈敏度。當石墨烯濃度為20%時，傳感器展現出9.7×10?? dB?1的高靈敏度，工作范圍覆蓋30至90 dB，聲壓級變化最小分辨率達10 dB，且聲壓級與傳感器測得的電阻變化呈線性相關。當作為可穿戴設備貼附于受試者喉部時，該傳感器能精準捕捉音色、節奏等細微發聲特征。結合基于支持向量機（SVM）的機器學習算法，該設備在識別數字（0-9）時達到95.9%的高準確率，助力語言障礙者實現數字化溝通。

圖文導讀

圖 1. (a) Fabrication flow of sensor ink; (b) schematic of the acoustic sensor fabricated using AJP; (c) schematic diagram of the wearable acoustic device preparation process; (d) schematic of the acoustic device; (e) device worn on the skin; (f) cross-sectional TEM image of the GC-20%G.

圖2. Schematic of the sensor conductive path at initial condition (a) and sound acoustic pressure-loaded environment (b).

圖3. Acoustic response of the sensor. (a) Response of sensors at varying sound pressure level; (b) schematic diagram of the change in tunneling path of sensors with lower and higher graphene concentrations under sound pressure; (c) frequency response of the GC-20%G sensor under a representative SPL of 80 dB with different frequencies (from 10 to 500 Hz); (d) real-time response signal of the sensor to the signal with gradually increasing sound pressure level; (e) relative resistance changes with the 1st and 100th cyclic sound pressure levels; (f) three acoustic waveforms from repeated playback of the same audio segment at 80 dB SPL: original loudspeaker signal, iPhone recording, and GC-20%G sensor output.

圖4. Acoustic device for voice detection. (a) Wearable acoustic device attached on the throat; (b) relative resistance change of the sensor when three different volunteers say, “Merry Christmas”; (c?h) relative resistance change of the sensor in response to three different sentences, compared with the microphone signal; (c, f) is the signal response of “The Hong Kong Polytechnic University”; (d, g) is the signal response of “Hope everything goes well with you”; and (e, h) is the signal response of “What a nice weather”.

圖5. Acoustic devices for digits 0–9 recognition. (a) Signal of digits from 0 to 9 captured by the sensor; (b) schematic flowchart of speech recognition; (c) classification confusion matrix of digit 0–9 prediction versus the test data set; (d) recognition of a sequence of digits “007”.

小結

本研究采用AJP工藝成功制備了新型壓阻式石墨烯/碳納米管（CNC）聲學傳感器，并采用聚氨酯薄膜封裝以增強柔韌性與貼合皮膚的能力。石墨烯濃度為20%（GC-20%G）的傳感器展現出9.7×10?? dB?1的最高靈敏度，以及10 dB的最小聲壓級變化分辨率，從而實現精準聲學檢測。該傳感器在30至90分貝聲壓級范圍內表現出低滯后特性與優異可逆性，并具備卓越的重復測量穩定性：局部最大響應與最小響應的標準差值僅分別為0.035和0.0349。該可穿戴聲學設備固定于受試者喉部，能可靠捕捉聲帶振動，從而精確識別語音模式、音調變化及獨特音頻信號。基于機器學習訓練的SVM模型能以95.9%的準確率識別傳感器采集的語音數字信號，證明其解讀語音信號語義內容的能力。這些成果彰顯了傳感器的檢測與識別潛力，為語障人士的通信技術應用拓展了廣闊前景。

展望未來，通過更大規模、更豐富多樣的數據集訓練模型，有望實現更復雜語音模式的識別——從數字擴展至完整詞匯與短語，從而拓展其在人機交互、醫療健康及無障礙技術領域的應用場景。此外，將運用分子動力學模擬深入探究微尺度隧道效應機制，通過定量分析為新一代高性能聲學傳感器的理性設計提供指導。

審核編輯 黃宇