Ultra-wide frequency response and high-resolution triboelectric acoustic sensors for constructing multifunctional speech aided system

IF 17.1 1区 材料科学 Q1 CHEMISTRY, PHYSICAL Nano Energy Pub Date : 2025-01-02 DOI:10.1016/j.nanoen.2024.110640
Tingwei Zheng , Mang Gao , Ying Wang , Yuguang Luo , Hao Zhang , Tengxiao Xiongsong , Jia Sun , Peihong Wang , Guozhang Dai , Junliang Yang
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Abstract

As an important medium for human-computer interaction, acoustic sensors can directly convey human intentions and provide information. However, it is challenging for existing acoustic sensors to simultaneously achieve a broad spectrum and high-resolution response to speech, which is crucial for accurately achieving human beings’ smart speech recognition for human-computer interaction in artificial intelligence (AI) era. In this work, we fabricate a high-performance triboelectric acoustic sensor (HPTS) equipped with an ultra-wide band response range, capable of collecting human speech across the entire range of audible frequencies from 20 Hz to 20,000 Hz, exhibiting ultra-high frequency resolution down to 0.1 Hz. Subsequently, a multifunctional speech aided system is constructed using HPTS that is capable of effectively achieving the recognition of subtle emotional nuances, semantics and voiceprint. Enhanced by the emotional semantic recognition model (ESRM), this speech aided system has achieved a much higher accuracy in recognizing seven different human emotions, reaching an impressive accuracy of 95.43 %. Additionally, it is proficient in interpreting various semantics and voiceprint from human users. This speech aided system with ultra-wideband response and ultra-high frequency resolution provides a highly promising solution for future human-computer interaction and has a wide range of potential applications in AI.

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构建多功能语音辅助系统的超宽频响高分辨率摩擦声传感器
声传感器作为人机交互的重要媒介,可以直接传达人的意图,提供信息。然而,现有的声学传感器很难同时实现对语音的广谱高分辨率响应,而这对于人工智能(AI)时代准确实现人类对人机交互的智能语音识别至关重要。在这项工作中,我们制造了一种高性能摩擦电声传感器(HPTS),配备了超宽带响应范围,能够在整个可听频率范围内收集人类语音,从20 Hz到20,000 Hz,显示出低至0.1 Hz的超高频率分辨率。随后,利用HPTS构建了一个多功能语音辅助系统,该系统能够有效地实现对细微情感差异、语义和声纹的识别。通过情感语义识别模型(ESRM)的增强,该语音辅助系统在识别七种不同的人类情感方面取得了更高的准确率,达到了令人印象深刻的95.43%。此外,它还能熟练地解释来自人类用户的各种语义和声纹。这种具有超宽带响应和超高频分辨率的语音辅助系统为未来的人机交互提供了非常有前途的解决方案,在人工智能中具有广泛的潜在应用前景。
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来源期刊
Nano Energy
Nano Energy CHEMISTRY, PHYSICAL-NANOSCIENCE & NANOTECHNOLOGY
CiteScore
30.30
自引率
7.40%
发文量
1207
审稿时长
23 days
期刊介绍: Nano Energy is a multidisciplinary, rapid-publication forum of original peer-reviewed contributions on the science and engineering of nanomaterials and nanodevices used in all forms of energy harvesting, conversion, storage, utilization and policy. Through its mixture of articles, reviews, communications, research news, and information on key developments, Nano Energy provides a comprehensive coverage of this exciting and dynamic field which joins nanoscience and nanotechnology with energy science. The journal is relevant to all those who are interested in nanomaterials solutions to the energy problem. Nano Energy publishes original experimental and theoretical research on all aspects of energy-related research which utilizes nanomaterials and nanotechnology. Manuscripts of four types are considered: review articles which inform readers of the latest research and advances in energy science; rapid communications which feature exciting research breakthroughs in the field; full-length articles which report comprehensive research developments; and news and opinions which comment on topical issues or express views on the developments in related fields.
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