EMG to speech mapping using scalogram-spectrogram features for silent speech recognition.

Authors

Dhanalakshmi Munirathnam, Shekinah Alvin, Sharmili Jayaseelan, Priyenga Sureshbabu

Published in

Journal of medical engineering & technology. Pages 1-19. Nov 01, 2026. Epub Nov 01, 2026.

Abstract

Silent speech Recognition systems (SSRs), which rely upon non-vocal signals like electromyography (EMG) from facial muscle tissues, offer a promising answer by permitting speech production without sound. This study seeks to enhance communication for those individuals by using a machine learning model, getting to know to correctly translate EMG signals from silent speech into audible, intelligible speech, thereby bridging the distance between non-verbal and verbal exchange. SSR improves the quality of life for those with speech impairments by providing necessary communication choices. This project investigates a whole new SSR method, which involves using surface electromyography signals (sEMG) and audio. A machine learning model was trained to recognize keywords via silent speech by an addition of a dataset of audio and sEMG signals. The scalograms and spectrograms are plotted for audio and EMG signal respectively and then it is mapped using the EfficientNet B0. It is then converted into textual content and then audible speech with the help of a speech synthesis system with an accuracy of 76%. This innovative technology opens new avenues in terms of silent communication, especially for customers who can talk without vocalizing, thereby improving accessibility and conversation for those who need it.

PMID:
42967225
Bibliographic data and abstract were imported from PubMed on 01 Nov 2026.

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