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Pilot study for a novel and personalized voice restoration device for patients with laryngectomy
Author(s) -
Rameau Anaïs
Publication year - 2020
Publication title -
head and neck
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.012
H-Index - 127
eISSN - 1097-0347
pISSN - 1043-3074
DOI - 10.1002/hed.26057
Subject(s) - laryngectomy , speech recognition , modality (human–computer interaction) , modalities , wearable computer , computer science , sentence , voice prosthesis , larynx , medicine , artificial intelligence , surgery , social science , sociology , embedded system
Background The main modalities for voice restoration after laryngectomy are the electrolarynx, and the tracheoesophageal puncture [Correction added on 30 January 2020 after first online publication: The preceding sentence has been revised. It originally read “The main modalities for voice restoration after laryngectomy are the electrolarynx and the tracheoesophageal puncture.”]. All have limitations and new technologies may offer innovative alternatives via silent speech. Objective To describe a novel and personalized method of voice restoration using machine learning applied to electromyographic signal from articulatory muscles for the recognition of silent speech in a patient with total laryngectomy. Methods Surface electromyographic (sEMG) signals of articulatory muscles were recorded from the face and neck of a patient with total laryngectomy who was articulating words silently. These sEMG signals were then used for automatic speech recognition via machine learning. Sensor placement was tailored to the patient's unique anatomy, following radiation and surgery. A personalized wearable mask covering the sensors was designed using 3D scanning and 3D printing. Results Using seven sEMG sensors on the patient's face and neck and two grounding electrodes, we recorded EMG data while he was mouthing “Tedd” and “Ed.” With data from 75 utterances for each of these words, we discriminated the sEMG signal with 86.4% accuracy using an XGBoost machine‐learning model. Conclusions This pilot study demonstrates the feasibility of sEMG‐based alaryngeal speech recognition, using tailored sensor placement and a personalized wearable device. Further refinement of this approach could allow translation of silently articulated speech into a synthesized voiced speech via portable devices.