Voice grafting using machine learning
OwnerRudolf MURAI VON BÜNAUinventor
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Abstract
A process labeled “voice grafting” can be understood in terms of the source-filter model of speech production as follows: For a patient who has partially or completely lost the ability to phonate, but retained at least a partial ability to articulate, the techniques described herein computationally “graft” the patient's time varying filter function, i.e. articulation, onto a source function, i.e. phonation, which is based on the speech output of one or more healthy speakers, in order to synthesize natural sounding speech in real time.
Claims28
A method for creating an artificial voice for a patient with missing or impaired phonation but at least residual articulation function, wherein an acoustic signal of one or more healthy speakers reading a known body of text out loud is recorded, at least one vocal tract signal of the patient mouthing the same known body of text is recorded, the acoustic signal and the at least one vocal tract signal are used to train a machine learning algorithm, and the machine learning algorithm is used in an electronic voice prosthesis measuring the patient's at least one vocal tract signal and converting it to an acoustic speech output in real time.
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A device for a patient with missing or impaired phonation but at least residual articulation function, wherein the device is configured to measure at least one vocal tract signal of the patient and to convert it to an acoustic speech output in real time using a machine learning algorithm, the machine learning algorithm having been trained with data that includes an acoustic signal of one or more healthy persons reading a body of text out loud and at least one vocal tract signal of one or more persons mouthing the same body of text.
Dependencies are inferred from the claim text.