Rameau A. Pilot study for a novel and personalized voice restoration device for patients with laryngectomy.
Head Neck 2019;
42:839-845. [PMID:
31876090 DOI:
10.1002/hed.26057]
[Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 11/06/2019] [Accepted: 12/10/2019] [Indexed: 01/04/2023] Open
Abstract
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.
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