Leveraging self-supervised speech representations for domain adaptation in speech enhancement

A server device comprising:

• a memory storing instructions;
• and
• at least one processor,
• wherein the instructions, when executed by the at least one processor, cause the server device to:
• obtain noisy-clean speech data from a source domain;
• obtain noisy speech data from a target domain;
• obtain raw speech data;
• using the noisy-clean speech data, the noisy speech data, and the raw speech data, train a customized SE model based on at least one of self-supervised representation-based adaptation (SSRA), ensemble mapping, or self-supervised adaptation loss;
• generate the customized SE model by denoising the noisy speech data using the trained customized SE model;
• and
• provide the customized SE model to a user device to use the denoised noisy speech data.

A non-transitory computer-readable recording medium configured to store instructions for generating a customized speech enhancement (SE) model, which, when executed by at least one processor of an electronic device, cause the at least one processor to perform a method comprising:

• obtaining noisy-clean speech data from a source domain;
• obtaining noisy speech data from a target domain;
• obtaining raw speech data;
• using the noisy-clean speech data, the noisy speech data, and the raw speech data, training the customized SE model based on at least one of self-supervised representation-based adaptation (SSRA), ensemble mapping, or self-supervised adaptation loss;
• generating the customized SE model by denoising the noisy speech data using the trained customized SE model;
• and
• providing the customized SE model to a user device to use the denoised noisy speech data.