Unsupervised non-parallel speech domain adaptation using a multi-discriminator adversarial network

A non-transitory machine-readable medium comprising a plurality of machine-readable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to perform a method comprising:

• providing a domain adaptation model configured to adapt a representation of a signal in a first domain to a second domain to generate an adapted presentation;
• and
• providing a plurality of discriminators corresponding to a plurality of bands,
• wherein each of the plurality of bands corresponds to a domain variable range of a domain variable of the first and second domains, and
• wherein each of the plurality of discriminators is configured to discriminate between the adapted representation and representations of one or more other signals in the second domain.

A method for training parameters of a first domain adaptation model using multiple independent discriminators, comprising:

• providing a plurality of first discriminator models corresponding to a plurality of first bands, each of the plurality of bands corresponding to a domain variable range of a domain variable of a source domain and a target domain;
• evaluating the plurality of first discriminator models based on:
• one or more first training representations adapted from the source domain to the target domain by the first domain adaptation model, and
• one or more second training representations in the target domain, yielding a first multi-discriminator objective;
• evaluating a learning objective based on the first multi-discriminator objective;
• and
• updating the parameters of the first domain adaptation model based on the learning objective.