Method of training grouping model for voice grouping, and voice noise reduction method

A voice grouping and voice noise reduction technology, applied in voice analysis, voice recognition, instruments, etc., can solve problems such as inappropriateness and different disturbance sizes, and achieve the effect of reducing the error rate

Active Publication Date: 2020-07-28
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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Problems solved by technology

Existing defense strategies treat all speech adversarial samples in the same

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  • Method of training grouping model for voice grouping, and voice noise reduction method
  • Method of training grouping model for voice grouping, and voice noise reduction method
  • Method of training grouping model for voice grouping, and voice noise reduction method

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Embodiment Construction

[0044] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0045] As mentioned in the background section, existing defense strategies treat all speech adversarial samples in the same way, which is inappropriate because the magnitude of perturbation added by different attacks may vary. In other words, the existing denoising methods only apply one method to denoise the audio, and the search space is relatively small, and different speech adversarial samples, due to the difference in transcription distance, the added disturbances are different in magnitude, the existing defense The strategy treats all speech adversarial samples in the same way, a...

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Abstract

The embodiment of the invention provides a method for training a grouping model for voice grouping, and a voice noise reduction method. The voice noise reduction method comprises the steps: B1, obtaining an input audio, and extracting the grouping features of the input audio; B2, inputting the grouping features of the input audio into a grouping model, and predictively outputting the label of theinput audio; and B3, according to the label of the input audio, taking the transcription text obtained by the audio processed by the optimal noise reduction strategy of the group corresponding to thelabel as the transcription text of the input audio. According to the technical scheme provided by the embodiment of the invention, the group to which the input audio belongs can be predicted accordingto the anti-disturbance of the input audio, and the noise reduction strategy suitable for the group is selected for noise reduction, so that the transcription error rate can be reduced, and the transcription quality of a clean sample cannot be influenced while the anti-transcription is recovered to the original transcription.

Description

technical field [0001] The present invention relates to the field of voice recognition, in particular to the field of voice recognition related to anti-attack processing, more specifically, to a method for training a grouping model for voice grouping and a voice noise reduction method. Background technique [0002] Deep neural networks (DNNs) have achieved impressive results in various artificial intelligence applications, including image classification, natural language processing, and speech recognition. In some domains, the performance of DNNs has reached or even surpassed human performance. Therefore, DNN is widely used in some safety-sensitive tasks that require high robustness of the model. [0003] However, in recent years, deep neural networks have been seriously threatened by adversarial attacks. Adversarial attacks generate adversarial samples by adding subtle noise to legitimate samples. While humans cannot recognize adversarial examples, they can make deep neu...

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Application Information

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IPC IPC(8): G10L15/20G10L15/26G10L15/02G10L15/06G10L21/0208
CPCG10L15/20G10L15/26G10L15/02G10L15/063G10L21/0208
Inventor 郭青丽叶靖胡瑜李晓维
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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