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.

Inclusion Criteria:

Group A: Healthy Volunteers

1. Adult subjects, 18 or older
2. Without any voice impairments

Group B: Subjects with Aphonia or Dysphonia

1. Adult subjects, 18 or older
2. Documentation of severe dysphonia and/or aphonia, or a GRBAS score > 0 (GRBAS is a scale that can be used to assess voice quality of subjects who do not have a recorded history of dysphonia or aphonia. The GRBAS scale evaluates for grade, roughness, breathiness, asthenia, and strain).

Exclusion Criteria:

- Group A: Healthy Volunteers

1. Voice impairment

Group B: Subjects with Aphonia or Dysphonia

1. Subjects whose face muscles are entirely paralyzed