The investigators will conduct a pilot experiment for a novel and personalized method for voice restoration using machine learning applied to surface EMG (sEMG) signal from articulatory muscles of the face and the neck allowing recognition of silent speech. The investigators predict that the use novel personalized method for voice restoration will be feasible and successful for patients.
This is a prospective pilot study evaluating the feasibility of a personalized voice restoration device and patients' experience with it. Study participation will include a one-time visit where subjects will read passages and phrases. Acoustic and signal data will be captured. Machine learning will be applied to the data to classify words. Subjects will also participate in a qualitative interview about their experience with voice restoration devices.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
DOUBLE
Enrollment
100
Surface ElectroMyoGraphy (SEMG) is a non-invasive technique for measuring muscle electrical activity that occurs during muscle contraction and relaxation cycles. Electrodes will be attached with a AgCl gel to muscles used for articulation.
Weill Cornell Medicine
New York, New York, United States
Percentage of Words and Numbers Correctly Identified
Accuracy of sEMG signal will be determined by subjects reciting common phrases. Subjects will recite the "Rainbow Passage" 10 times. Subjects will be asked to count from 1-10 once. Subjects will recite the alphabet once.
Time frame: "During Procedure/use of Surface ElectroMyoGraphy"
Subject experience with voice restoration devices: Qualitative
Subject will be asked to complete a semi-structured interview about their experience with voice restoration devices. Categories of response include voice/communication history, experience with previous voice restoration devices, experience with our device, and suggestions for improvement.
Time frame: 10 minutes after the time of intervention
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