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Speech synthesis from three-axis accelerometer signals using conformer-based deep neural network.

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This study explores synthesizing speech from facial movements using accelerometers. Adding a fifth sensor significantly improved speech synthesis quality, paving the way for practical silent speech interfaces.

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Area of Science:

  • Biomedical Engineering
  • Speech Technology
  • Signal Processing

Background:

  • Silent speech interfaces (SSIs) offer non-acoustic communication, with previous work showing promise for accelerometer-based systems.
  • Accelerometer-based SSIs outperformed surface electromyography (sEMG) systems in identifying silently spoken words.

Purpose of the Study:

  • To investigate the feasibility of synthesizing spoken speech from three-axis accelerometer signals.
  • To assess the potential of accelerometer-based SSIs for practical silent communication applications.

Main Methods:

  • Nineteen healthy participants wore five accelerometers on their face to capture speech-related movements.
  • A convolution-augmented Transformer (Conformer) model converted accelerometer signals to Mel spectrograms, with audio synthesized via HiFi-GAN.
  • Ten-fold cross-validation and Mel cepstral distortion (MCD) were used to evaluate Mel spectrogram quality.

Main Results:

  • An average MCD of 5.03 ± 0.65 was achieved with four optimized accelerometers.
  • Adding a fifth accelerometer under the chin significantly improved Mel spectrogram quality to an average MCD of 4.86 ± 0.65 (p < 0.001).
  • The results surpass conventional SSIs using fewer sensors and comparable training data.

Conclusions:

  • Synthesizing speech from accelerometer signals is feasible and shows high quality.
  • The proposed method offers a low-power, portable, and artifact-resistant alternative to existing SSIs.
  • This advancement could drive the wider adoption of accelerometer-based silent speech interfaces.