Speech signal processing model training method, electronic device and storage medium

An electronic device, comprising:

• at least one memory and at least one processor;
• the memory storing a program, the processor invoking the program stored by the memory, and the program being configured for:
• acquiring a sample speech and determining task input features each for one speech signal processing task among one or more speech signal processing tasks for the sample speech;
• establishing a starting multi-task neural network model comprising one or more task layers corresponding to the one or more speech signal processing tasks and a shared layer common to the one or more speech signal processing tasks;
• determining a target training loss function based on separate training loss functions each for one speech signal processing task among the one or more speech signal processing tasks;
• and
• using the task input features of the one or more speech signal processing tasks as a training input of the starting multi-task neural network, and updating model parameters of the shared layer and the one or more task layers of the starting multi-task neural network by minimizing the target training loss function as a training objective, until the starting multi-task neural network converges, to obtain a speech signal processing model,

A storage medium, storing a program configured to be executed by a processor, the program being configured for:

• acquiring a sample speech and determining task input features each for one speech signal processing task among one or more speech signal processing tasks for the sample speech;
• establishing a starting multi-task neural network comprising one or more task layers corresponding to the one or more speech signal processing tasks and a shared layer common to the one or more speech signal processing tasks;
• determining a target training loss function based on separate training loss functions each for one speech signal processing task among the one or more speech signal processing tasks;
• and
• using the task input features of the one or more speech signal processing tasks as a training input of the starting multi-task neural network, and updating model parameters of the shared layer and the one or more task layers of the starting multi-task neural network by minimizing the target training loss function as a training objective, until the starting multi-task neural network converges, to obtain a speech signal processing model.