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Summary

This review explores mouth interface technologies and deep learning for speech recognition and production. These advancements aim to restore communication for individuals with voice impairments, improving their quality of life.

Keywords:
EMGartificial larynxbiosignaldeep learningmouth interfacevoice production

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Speech Technology

Background:

  • Voice is crucial for human communication, but voice inability can lead to social isolation.
  • Increasing risks of voice loss necessitate novel speech recognition and production solutions.

Purpose of the Study:

  • To review mouth interface technologies for speech recognition, production, and volitional control.
  • To survey research on artificial mouth technologies using diverse sensor data and deep learning.
  • To analyze deep learning applications in voice recognition, including visual and silent speech interfaces.

Main Methods:

  • Systematic review of mouth-mounted devices and sensor technologies (EMG, EEG, EPG, EMA, PMA, gyros, imaging, magnetic sensors).
  • Analysis of deep learning techniques applied to speech recognition and production.
  • Taxonomic organization of visual speech recognition and silent speech interface research.

Main Results:

  • Mouth interface technologies offer potential for speech recognition and production.
  • Deep learning significantly enhances the performance of various speech-related sensor technologies.
  • Visual and silent speech interfaces show promise for communication restoration.

Conclusions:

  • Mouth interface technologies, powered by deep learning, are vital for addressing communication challenges in individuals with speaking disabilities.
  • Further research into deep learning components is essential for advancing artificial mouth technologies and improving quality of life.