Voice modification

A method comprising:

• processing, with an encoder of a machine learning system, an input audio waveform comprising first utterances by a speaker to generate an encoder output, the first utterances having a first accent;
• processing, with a decoder of the machine learning system, the encoder output to generate an output audio waveform comprising second utterances, the second utterances having a second accent different from the first accent;
• computing, with an identification loss discriminator of the machine learning system, based on the input audio waveform and the output audio waveform, an identification loss for the output audio waveform;
• computing, with a text loss discriminator of the machine learning system, based on the input audio waveform and the output audio waveform, a text loss for the output audio waveform;
• and
• training the decoder using the identification loss and the text loss, wherein the trained decoder is configured to modify speaker utterances based on a target accent and a differential weighting between a speaker identification loss function that applies a penalty based on a speaker identity and a text loss function that applies a penalty for loss of intelligibility.

A non-transitory computer-readable storage medium comprising instructions that, when executed, cause one or more processors of a computing device to:

• receive an input audio waveform comprising utterances by a speaker;
• and
• execute a machine learning system,
• wherein the machine learning system is configured to implement an autoencoder, the autoencoder comprising a decoder trained to characterize an accent for a target accent, the decoder configured to modify the utterances by the speaker based on the target accent and a differential weighting between a speaker identification (ID) loss function that applies a penalty based on an identity for the speaker and a text loss function that applies a penalty for loss of intelligibility,
• wherein the machine learning system is configured to process the input audio waveform to generate an output audio waveform that includes the modified utterances, and
• wherein the machine learning system is configured to output the output audio waveform.