Training teacher machine learning models using lossless and lossy branches

A computer program product (CPP) comprising:

• a machine readable storage medium;
• and
• computer code stored on the machine readable storage medium, with the computer code including instructions for causing a processor set to perform operations including the following:
• receiving a training data set, with the training data set including at least one lossy audio recording, at least one lossless audio recording and a corresponding label,
• training a teacher neural network with at least a first branch and a second branch, wherein training the teacher neural network comprises:
• determining a first branch output using the first branch to process the at least one lossless audio recording;
• determining a second branch output using the second branch to process the at least one lossy audio recording;
• quantifying a first loss value as a difference between the corresponding label and the first branch output;
• quantifying a second loss value as a difference between the second branch output and the first branch output;
• and
• modifying weights for the first branch and the second branch based on a sum of the first loss value and the second loss value;
• and
• responsive to receiving a test input data set including a lossy audio recording, generating a set of soft-targets for training a student network based, at least in part, on the second branch and the test input dataset.

A computer system (CS) comprising:

• a processor set;
• a machine readable storage medium;
• and
• computer code stored on the machine readable storage medium, with the computer code including instructions for causing the processor set to perform operations including the following:
• receiving a training data set, with the training data set including at least one lossy audio recording, at least one lossless audio recording and a corresponding label,
• training a teacher neural network with at least a first branch and a second branch, wherein training the teacher neural network comprises:
• determining a first branch output using the first branch to process the at least one lossless audio recording;
• determining a second branch output using the second branch to process the at least one lossy audio recording;
• quantifying a first loss value as a difference between the corresponding label and the first branch output;
• quantifying a second loss value as a difference between the second branch output and the first branch output;
• and
• modifying weights for the first branch and the second branch based on a sum of the first loss value and the second loss value;
• and
• responsive to receiving a test input data set including a lossy audio recording, generating a set of soft-targets for training a student network based, at least in part, on the second branch and the test input dataset.