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Mixed-modality speech recognition and interaction using a wearable artificial throat

Nature Machine Intelligence volume 5, pages 169–180 (2023)Cite this article

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Abstract

Researchers have recently been pursuing technologies for universal speech recognition and interaction that can work well with subtle sounds or noisy environments. Multichannel acoustic sensors can improve the accuracy of recognition of sound but lead to large devices that cannot be worn. To solve this problem, we propose a graphene-based intelligent, wearable artificial throat (AT) that is sensitive to human speech and vocalization-related motions. Its perception of the mixed modalities of acoustic signals and mechanical motions enables the AT to acquire signals with a low fundamental frequency while remaining noise resistant. The experimental results showed that the mixed-modality AT can detect basic speech elements (phonemes, tones and words) with an average accuracy of 99.05%. We further demonstrated its interactive applications for speech recognition and voice reproduction for the vocally disabled. It was able to recognize everyday words vaguely spoken by a patient with laryngectomy with an accuracy of over 90% through an ensemble AI model. The recognized content was synthesized into speech and played on the AT to rehabilitate the capability of the patient for vocalization. Its feasible fabrication process, stable performance, resistance to noise and integrated vocalization make the AT a promising tool for next-generation speech recognition and interaction systems.

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Fig. 1: Speech interaction paradigm based on the wearable AT.
Fig. 2: Device design and characterization.
Fig. 3: Settings and composition of the datasets used for the comparative experiments.
Fig. 4: Metrics of recognition-related performance in the comparative experiments.
Fig. 5: Sentences collected from a volunteer patient recovering from laryngectomy and the results of recognition.
Fig. 6: Rehabilitating the vocal capability of a laryngectomy patient by using an ensemble AI model.

Data availability

The collected AT speech spectrums for classification in Fig. 4a are provided via Zenodo (https://doi.org/10.5281/zenodo.7396184)45. A detailed manual for the data analysis and deployment can be found in Supplementary Note 1 and the Zenodo repository. Other study findings are available from T.-L.R. on reasonable request, and in accordance with the Institutional Review Board at Tsinghua University. Source Data are provided with this paper.

Code availability

The code used to implement the classification tasks and the ensemble model in this study are available from Zenodo (https://doi.org/10.5281/zenodo.7396184)45.

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Acknowledgements

This work was supported in part by: the National Natural Science Foundation of China under grant nos. 62022047 and 61874065 (to H.T.), and U20A20168 and 51861145202 (to T.R.); the National Key R&D Program (grant nos. 2022YFB3204100, 2021YFC3002200 and 2020YFA0709800); JIAOT (grant no. KF202204); the STI 2030—Major Projects (grant no. 2022ZD0209200 to H.T.); the Fok Ying-Tong Education Foundation (grant no. 171051 to H.T.); the Beijing Natural Science Foundation (grant no. M22020 to H.T.); the Beijing National Research Center for Information Science and Technology Youth Innovation Fund (grant no. BNR2021RC01007 to H.T.); the State Key Laboratory of New Ceramic and Fine Processing Tsinghua University (grant no. KF202109 to H.T.); the Tsinghua–Foshan Innovation Special Fund (TFISF) (grant no. 2021THFS0217 to H.T.); the Research Fund from Beijing Innovation Center for Future Chips (T.R.); the Center for Flexible Electronics Technology, Daikin–Tsinghua Union Program of ‘The Etching Rates of Different Gases’; and the Independent Research Program of Tsinghua University (grant no. 20193080047 to H.T.). This work is also supported by the Opening Project of the Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences (H.T.); the Tsinghua–Toyota Joint Research Fund (H.T.); and the Guoqiang Institute, Tsinghua University (T.R.).

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  1. These authors contributed equally: Qisheng Yang, Weiqiu Jin.

Authors and Affiliations

  1. School of Integrated Circuits and Beijing National Research on Information Science and Technology (BNRist), Tsinghua University, Beijing, China

    Qisheng Yang, Qihang Zhang, Yuhong Wei, Zhanfeng Guo, Xiaoshi Li, Yi Yang, He Tian & Tian-Ling Ren

  2. Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China

    Weiqiu Jin & Qingquan Luo

  3. School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Weiqiu Jin & Qingquan Luo

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Contributions

Q.Y., W.J. and H.T. conceived and designed the experiments. Q.S., W.J. and Q.H. contributed to sensor fabrication and dataset collection. Q.Y., Q.H., W.Y., G.Z. and X.S. performed data characterizations. G.Z. simulated the strain distribution of the honeycomb structure. W.J. performed the classification task using AI methods. Q.S. and W.J. analysed the data and wrote the paper. T.R., H.T., Y.Y. and Q.L. supervised the project. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Yi Yang, Qingquan Luo, He Tian or Tian-Ling Ren.

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Nature Machine Intelligence thanks Masafumi Nishimura and Huijun Ding for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Note 1, Figs. 1–19 and Tables 1–9.

Reporting Summary

Demonstration of the speech perception interference and recognition experiments.

The recognized daily short sentences of a laryngectomy patient generated by the graphene AT.

Source data

Source Data Fig. 2

Statistical Source Data.

Source Data Fig. 3

Statistical Source Data.

Source Data Fig. 4

Statistical Source Data.

Source Data Fig. 5

Statistical Source Data.

Source Data Fig. 6

Statistical Source Data.

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Yang, Q., Jin, W., Zhang, Q. et al. Mixed-modality speech recognition and interaction using a wearable artificial throat. Nat Mach Intell 5, 169–180 (2023). https://doi.org/10.1038/s42256-023-00616-6

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