Baidu Online Network Technology (beijing) Co., Ltd.

Weiwei Cui

Xiangang Li

Wei Zou

Jingyuan Hu

Yu Sun

Embodiments of the present disclosure provide a speech noise reduction method and a speech noise reduction device based on artificial intelligence and a computer device. The method includes the followings. A first noisy speech to be processed is received. The first noisy speech to be processed is pre-processed, to obtain the first noisy speech in a preset format. The first noisy speech in the preset format is sampled according to a sampling rate indicated by the preset format, to obtain first sampling point information of the first noisy speech. A noise reduction is performed on the first sampling point information through a deep-learning noise reduction model, to generate noise-reduced first sampling point information. A first clean speech is generated according to the noise-reduced first sampling point information.

Speech noise reduction method and device based on artificial intelligence and computer device

The present application discloses a voice recognition method and apparatus. A specific implementation of the method includes: in response to detecting a microphone receiving voice signal containing interfering sound signal, performing high-pass filtering on the voice 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. This implementation improves the success rate of voice recognition.

Voice recognition method and apparatus

The present disclosure provides a method and a device for processing a speech based on artificial intelligence. The method includes: receiving a speech processing request, in which the speech processing request includes a first digital speech signal and a first sample frequency corresponding to the first digital speech signal; selecting a target speech processing model from a pre-trained speech processing model base according to the first sample frequency; performing up-sampling processing on the first digital speech signal using the target speech processing model to generate a second digital speech signal having a second sample frequency, in which the second sample frequency is larger than the first sample frequency.

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Beijing, China

Weiwei Cui