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Work-in-Progress: Silent Speech Recognition Interface for the Differently Abled

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  • First Online:
Cyber-physical Systems and Digital Twins (REV2019 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 80))

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Abstract

Silent speech or unvoiced speech can be interpreted by lip reading, which is difficult, or by using EMG (Electromyography) electrodes to convert the facial muscle movements into distinct signals. These signals are processed in MATLAB and matched to a predefined word by using Dynamic Time Warping algorithm. The identified word is then converted to speech and can be used to control a nearby device such as a motorized wheelchair. Thus, a silent speech interface has the potential to enable a differently-abled person to communicate and interact with objects in their surroundings to ease their lives.

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References

  1. National Institute on Deafness and Other Communication Disorders: Retrieved from https://www.nidcd.nih.gov/health/statistics/quick-statistics-voice-speech-language (2010)

  2. Juang, B.H.: On the hidden Markov model and dynamic time warping for speech recognition—a unified view. AT&T Bell Laboratories Tech. J. 63(7), 1213–1243 (1984)

    Article  MathSciNet  Google Scholar 

  3. Wand, M., Schultz, T.: Session-Independent EMG-Based Speech Recognition (2011)

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  4. Kapur, A., Kapur, S., Maes, P.: AlterEgo: a personalized wearable silent speech interface. In: 23rd International Conference on Intelligent User Interfaces (IUI 2018), 5 Mar 2018

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  5. Gaikwad, S.K., Gawali, B.W., Yannawar, P.: A review on speech recognition technique. Int. J. Comput. Appl. (0975–8887) (2010)

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Author information

Authors and Affiliations

  1. The Oxford College of Engineering, Bengaluru, Karnataka, India

    Josh Elias Joy, H. Ajay Yadukrishnan, V. Poojith & J. Prathap

Authors
  1. Josh Elias Joy
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  2. H. Ajay Yadukrishnan
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  3. V. Poojith
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  4. J. Prathap
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Corresponding author

Correspondence to Josh Elias Joy .

Editor information

Editors and Affiliations

  1. Carinthia University of Applied Sciences, Villach, Austria

    Michael E. Auer

  2. Electrono Solutions Pvt. Ltd., Whitefield, Bengaluru, India

    Kalyan Ram B.

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Joy, J.E., Ajay Yadukrishnan, H., Poojith, V., Prathap, J. (2020). Work-in-Progress: Silent Speech Recognition Interface for the Differently Abled. In: Auer, M., Ram B., K. (eds) Cyber-physical Systems and Digital Twins. REV2019 2019. Lecture Notes in Networks and Systems, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-23162-0_73

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