Voice activity detection method, method for establishing voice activity detection model, computer device, and storage medium

A computer device, comprising:

• at least one memory configured to store computer program code;
• and
• at least one processor configured to access the computer program code and operate as instructed by the computer program code, the computer program code comprising:
• file obtaining code configured to cause the at least one processor to obtain a training audio file;
• result obtaining code configured to cause the at least one processor to obtain a target result of the training audio file;
• framing code configured to cause the at least one processor to frame the training audio file to obtain an audio frame;
• extraction code configured to cause the at least one processor to extract an audio feature of the audio frame, the audio feature comprising at least two types of features, and one of the at least two types of features comprising an energy;
• inputting code configured to cause the at least one processor to input the audio feature as an input to a deep neural network model, and perform information processing on the audio feature through a hidden layer of the deep neural network model;
• outputting code configured to cause the at least one processor to output the processed audio feature through an output layer of the deep neural network model, to obtain a training result;
• and
• update and optimizing code configured to cause the at least one processor to determine a bias between the training result and the target result as an input to an error back propagation mechanism, and separately update weights of the hidden layer until the deep neural network model reaches a preset condition, to obtain a voice activity detection model.

A non-transitory computer-readable storage medium, storing executable instructions, the executable instructions capable of causing a computer to:

• obtain a training audio file and a target result of the training audio file;
• frame the training audio file to obtain an audio frame;
• extract an audio feature of the audio frame, the audio feature comprising at least two types of features, and one of the at least two types of features comprising an energy;
• input the audio feature as an input to a deep neural network model, performing information processing on the audio feature through a hidden layer of the deep neural network model, and output the processed audio feature through an output layer of the deep neural network model, to obtain a training result;
• and
• determine a bias between the training result and the target result as an input to an error back propagation mechanism, and separately update weights of the hidden layer until the deep neural network model reaches a preset condition, to obtain a voice activity detection model.