Evidence map›Paper›PMID 41603116›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

A Wireless, Battery-Free Artificial Throat Patch with Deep Learning for Emotional Speech Recognition.

Bingxin Xu, Guanming Lin, Xiaohui Hong, Chuting Liu, Xinyi Qu, Kangjian Jiao, Lei Zhang, Bingzhe Xu, Jun Yan, Yancong Qiao and 1 more

Abstract read
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In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Bingxin XuGuangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China.
Guanming LinGuangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China.
Xiaohui HongGuangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China.
Chuting LiuGuangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China.
Xinyi QuGuangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China.
Kangjian JiaoGuangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China.
Lei ZhangGuangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China.
Bingzhe XuGuangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China.
Jun YanThe Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
Yancong QiaoGuangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China.
Xudong LinGuangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China.ORCID https://orcid.org/0000-0001-5479-4414

Funding

2021 Foshan Science and Technology Innovation Team for Young-Top Talents 2120001010795Foundation of Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument 2020B1212060077National Natural Science Foundation of China 62201624National Natural Science Foundation of China 82372088National Natural Science Foundation of Guangdong Province 2025B1515020035Shenzhen Science and Technology Program 202206193000001Shenzhen Science and Technology Program 20220816161126002Shenzhen Science and Technology Program 202408133004546Shenzhen Science and Technology Program RCBS20221008093310024
6 · The paper itself

Abstract

Technologies for sensing and translating throat signals into speech have advanced the communication for people with vocal impairments. However, the practical deployment of these technologies is currently hindered by tethered system, bulky components and the lack of emotional recognition capability. Here, we demonstrate a wireless, battery-free artificial throat patch system (ATPS) coupling with deep learning method to enable simultaneous recognition of speech and emotion. The sensing module of the ATPS integrates a carbon nanotube-based thin-film strain sensor and a miniaturized flexible printed circuit board (FPCB). Real-time sensing and transmission of throat signals is facilitated by a smartphone-linked system incorporating an FPCB with an embedded near-field communication antenna and low-power electronic components. Furthermore, the information-rich sensor readouts are further extracted and successfully demonstrated to be sufficient to emotional recognition of speech by implementing a hybrid deep learning architecture. This proposed system expands the arsenal of tools available for mute patients in speech recognition and communication and further realizes personalized clinical implementations.

Indexed as

Deep LearningEmotionsPharynxSpeechWireless TechnologyHumansartificial throatbioelectronicsdeep learningemotion recoginitionspeech recoginition

Identifiers

PMID41603116
PMCPMC13045234

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.