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

Beijing Baidu Netcom Science and Technology Co., Ltd.

Weixin Zhu

Chao Li

Ming Wen

Xin Li

Jianwei Sun

Embodiments of the present disclosure include methods, apparatuses, devices, and computer readable storage mediums for processing far-field environmental noise. The method can comprise processing collected far-field environmental noise to a noise segment in a predetermined format. The method can further comprise establishing a far-field voice recognition model based on the noise segment and a near-field voice segment; and determining validity of the noise segment based on the far-field voice recognition model. The solution of the present disclosure can optimize anti-noise performance of the far-field voice recognition model by differentiated training of noise in different user scenarios of a far-field voice recognition product.

Method, apparatus, device and storage medium for processing far-field environmental noise

A voice recognition method is provided by embodiments of the present application. The method includes: obtaining a voice signal to be recognized; and recognizing a current frame in the voice signal using a pre-trained causal acoustic model, according to the current frame in the voice signal and a frame within a preset time period before the current frame, the causal acoustic model being derived based on a causal convolutional neural network training. In the method provided by the embodiments of the present application, only the information of the current frame and the frame before the current frame is used when performing the recognition of the current frame, thereby solving a problem in voice recognition technologies based on prior art convolutional neural network where a hard delay is created because there is a need to wait for the frames after the current frame, improving the timeliness of the voice recognition.

Voice recognition method, apparatus, device and storage medium

A voice activity detection method and an apparatus are provided by embodiments of the present application. The method includes: performing framing processing on a voice to be detected to obtain a plurality of audio frames to be detected; obtaining an acoustic feature of each of the audio frames to be detected, and sequentially inputting the acoustic feature of the each of the audio frames to be detected to a VAD model, wherein the VAD model is configured to classify a first N voice frame in the voice to be detected as a noise frame, classify frames from an (N+1)-th voice frame to a last voice frame as voice frames, and classify a M noise frame after the last voice frame as a voice frame, where N and M are integers; and determining, according to a classification result output by the VAD model.

Voice activity detection method and apparatus

Embodiments of the present disclosure provide a method and a device for generating far-field speech data, a computer device and a computer readable storage medium. The method includes obtaining environmental noise in real environment and adjusting near-field speech data in a near-field speech data set based on the environmental noise, further includes generating far-field speech data based on adjusted near-field speech data and the environmental noise.

Method and device for generating far-field speech data, computer device and computer readable storage medium

The present disclosure provides a method, an apparatus and a device for recognizing voice endpoints. In the method of the present disclosure, a start point recognition model and a finish point recognition model are obtained by training a cyclic neural network with a start point training set and a finish point training set, respectively, and a voice start point frame among audio frames is recognized according to each of acoustic features of the audio frames and the start point recognition model, thereby avoiding affecting a delay time of the finish point frame recognition while ensuring the accuracy of the start frame recognition as high as possible; and a voice finish point frame among the audio frames is recognized according to the acoustic features of the audio frames and the finish point recognition model.

Method, apparatus and device for recognizing voice endpoints

The present application provides a voice processing method, an apparatus, a device, and a storage medium, including: acquiring a first acoustic feature of each of N voice frames, where N is a positive integer greater than 1; applying a neural network algorithm to N first acoustic features to obtain a first mask; modifying the first mask according to VAD information of the N voice frames to obtain a second mask; and processing the N first acoustic features according to the second mask to obtain a second acoustic feature, resulting in more effective noise suppression and a lower damage to the voice.

Voice processing method, apparatus, device and storage medium

The present disclosure provides a method and apparatus for detecting a starting point and a finishing point of a speech, a computer device and a storage medium, wherein the method comprises: obtaining speech data to be detected; segmenting the speech data into speech segments, the number of speech segments being greater than one; respectively determining speech states of respective speech segments based on a Voice Activity Detection model obtained by pre-training; determining a starting point and a finishing point of the speech data according to the speech states. The solution of the present disclosure can be employed to improve the accuracy of the detection results.

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Beijing, China

Weixin Zhu