With the development of science and technology, human–machine interaction has brought great benefits to the society. Here, we design a voice and gesture signal translator (VGST), which can translate natural actions into electrical signals and realize efficient communication in human–machine interface. By spraying silk protein on the copper of the device, the VGST can achieve improved output and a wide frequency response of 20–2000 Hz with a high sensitivity of 167 mV/dB, and the resolution of frequency detection can reach 0.1 Hz. By designing its internal structure, its resonant frequency and output voltage can be adjusted. The VGST can be used as a high-fidelity platform to effectively recover recorded music and can also be combined with machine learning algorithms to realize the function of speech recognition with a high accuracy rate of 97%. It also has good antinoise performance to recognize speech correctly even in noisy environments. Meanwhile, in gesture recognition, the triboelectric translator is able to recognize simple hand gestures and to judge the distance between hand and the VGST based on the principle of electrostatic induction. This work demonstrates that triboelectric nanogenerator (TENG) technology can have great application prospects and significant advantages in human–machine interaction and high-fidelity platforms.

中文翻译：

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摩擦纳米发电机和机器学习支持语音和手势双重模式的人机交互

随着科学技术的发展，人机交互给社会带来了巨大的利益。在这里，我们设计了一种语音和手势信号转换器 (VGST)，可以将自然动作转换为电信号，实现人机界面中的高效通信。通过在器件的铜上喷涂丝蛋白，VGST 可以实现更高的输出和 20-2000 Hz 的宽频率响应，灵敏度高达 167 mV/dB，频率检测分辨率可达 0.1 Hz。通过设计其内部结构，可以调节其谐振频率和输出电压。VGST可以作为高保真平台，有效恢复录制的音乐，也可以结合机器学习算法实现语音识别功能，准确率高达97%。它还具有良好的抗噪声性能，即使在嘈杂的环境中也能正确识别语音。同时，在手势识别方面，摩擦起电翻译器能够识别简单的手势，并根据静电感应原理判断手与VGST的距离。这项工作表明摩擦纳米发电机（TENG）技术在人机交互和高保真平台方面具有广阔的应用前景和显着优势。