Speech separation model training method and apparatus, storage medium and computer device

A computer device, comprising a memory and a processor, the memory storing computer-readable instructions, the computer-readable instructions, when executed by the processor, causing the computer device to perform a speech separation model training method including:

• obtaining first audio and second audio, the first audio comprising target audio and having corresponding labeled audio, and the second audio comprising noise audio;
• obtaining an encoding model, an extraction model, and an initial estimation model, an output of the encoding model being an input of the extraction model, the output of the encoding model and an output of the extraction model being jointly inputs of the estimation model;
• performing unsupervised training on the encoding model, the extraction model, and the estimation model according to the second audio, and adjusting model parameters of the extraction model and the estimation model;
• performing supervised training on the encoding model and the extraction model according to the first audio and the labeled audio corresponding to the first audio, and adjusting a model parameter of the encoding model;
• and
• continuously performing the unsupervised training and the supervised training in an alternating manner until a training stop condition is met.

A non-transitory computer-readable storage medium storing computer-readable instructions, the computer-readable instructions, when executed by one or more processors of a computer device, causing the computer device to perform a speech separation model training method including:

• obtaining first audio and second audio, the first audio comprising target audio and having corresponding labeled audio, and the second audio comprising noise audio;
• obtaining an encoding model, an extraction model, and an initial estimation model, an output of the encoding model being an input of the extraction model, the output of the encoding model and an output of the extraction model being jointly inputs of the estimation model;
• performing unsupervised training on the encoding model, the extraction model, and the estimation model according to the second audio, and adjusting model parameters of the extraction model and the estimation model;
• performing supervised training on the encoding model and the extraction model according to the first audio and the labeled audio corresponding to the first audio, and adjusting a model parameter of the encoding model;
• and
• continuously performing the unsupervised training and the supervised training in an alternating manner until a training stop condition is met.