Voice recognition method and apparatus

A voice recognition apparatus for a terminal device, the terminal device being equipped with a microphone, the apparatus comprising:

• at least one processor;
• and
• a memory storing instructions, which when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:
• performing high-pass filtering on the voice signal, in response to detecting the microphone receiving voice signal containing interfering sound signal;
• cancelling the interfering sound signal in the voice signal subjected to high-pass filtering;
• performing automatic gain control on the voice signal subjected to cancelling the interfering sound signal, to obtain target voice signal;
• and
• extracting a feature vector from the target voice signal and input the feature vector into a pre-trained acoustic model, to obtain a voice recognition result matching the target voice signal, the acoustic model being used for representing a corresponding relationship between the feature vector and the voice recognition result, wherein before the performing high-pass filtering on the voice signals, in response to detecting the microphone receiving the voice signal, the operations further comprise:
• pre-processing a pre-acquired training sample to generate a target training sample, the target training sample comprising a voice identifier;
• extracting the feature vector from the target training sample;
• and
• training, using a convolutional neural network, a deep neural network, and a restricted Boltzmann machine, and assigning the feature vector extracted from the target training sample as an input and the voice identifier as an output, to obtain the acoustic model.

A non-transitory computer storage medium storing a computer program, which when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising:

• performing high-pass filtering on a voice signal, in response to detecting the microphone receiving the voice signal containing interfering sound signal;
• cancelling the interfering sound signal in the voice signal subjected to high-pass filtering;
• performing automatic gain control on the voice signal subjected to cancelling the interfering sound signal, to obtain target voice signal;
• and
• extracting a feature vector from the target voice signal and inputting the feature vector into a pre-trained acoustic model, to obtain a voice recognition result matching the target voice signal, the acoustic model being used for representing a corresponding relationship between the feature vector and the voice recognition result, wherein before the performing high-pass filtering on the voice signals, in response to detecting the microphone receiving the voice signal, the operations further comprise:
• pre-processing a pre-acquired training sample to generate a target training sample, the target training sample comprising a voice identifier;
• extracting the feature vector from the target training sample;
• and
• training, using a convolutional neural network, a deep neural network, and a restricted Boltzmann machine, and assigning the feature vector extracted from the target training sample as an input and the voice identifier as an output, to obtain the acoustic model.