Back to all papers

Toward Non-Invasive Voice Restoration: A Deep Learning Approach Using Real-Time MRI

Authors

Saleh, M. W.

Affiliations (1)

  • University of Balamand

Abstract

Despite recent advances in brain-computer interfaces (BCIs) for speech restoration, existing systems remain invasive, costly, and inaccessible to individuals with congenital mutism or neurodegenerative disease. We present a proof-of-concept pipeline that synthesizes personalized speech directly from real-time magnetic resonance imaging (rtMRI) of the vocal tract, without requiring acoustic input. Segmented rtMRI frames are mapped to articulatory class representations using a Pix2Pix conditional GAN, which are then transformed into synthetic audio waveforms by a convolutional neural network modeling the articulatory-to-acoustic relationship. The outputs are rendered into audible form and evaluated with speaker-similarity metrics derived from Resemblyzer embeddings. While preliminary, our results suggest that even silent articulatory motion encodes sufficient information to approximate a speakers vocal characteristics, offering a non-invasive direction for future speech restoration in individuals who have lost or never developed voice.

Topics

radiology and imaging

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.