Iterative denoising method based on speech recognition and chip

Abstract

The invention discloses an iterative denoising method based on speech recognition and a chip. The method includes the following steps: S1, determining a target speech signal and a target confidence value thereof; S2, selecting noise data matching the target confidence value from a noise database, and making the noise data and the unmarked voiced frames in the target speech signal participate in apre-denoising process; S3, judging whether the pre-denoising result is greater than a predetermined threshold, going to S4 if the pre-denoising result is greater than the predetermined threshold, otherwise, going to S5; S4, marking the voiced frames corresponding to the pre-denoising result as denoised voiced frames in the target speech signal; and S5, judging whether the absolute value of the difference between the confidence value of the pre-denoising result and the target confidence value is smaller than a confidence threshold, marking the voiced frames corresponding to the pre-denoising result as denoised voiced frames in the target speech signal if the absolute value of the difference is smaller than the confidence threshold, otherwise, adjusting the target confidence value and returning to S2.

Description

technical field [0001] The invention belongs to the technical field of robots, and in particular relates to a speech recognition-based iterative denoising method and a chip. Background technique [0002] Existing mobile robots (such as mechatronics equipment such as sweeping robots, window cleaning robots, and floor mopping robots among cleaning robots) will generate noise during work. The voice signal is picked up by voice, but when the voice signal from the user is picked up, the noise generated during the robot's work is usually picked up by voice, so that the voice signal picked up by the device is mixed with a lot of external noise , the corresponding speech recognition accuracy is not high, which will seriously affect the robot's recognition of external speech (effective signal), and make logical decisions based on speech interpretation (such as performing relevant path planning). [0003] In the prior art, the method of denoising at the front end of the voice signal ...

AI Technical Summary

Problems solved by technology

[0002] Existing mobile robots (such as mechatronics equipment such as sweeping robots, window cleaning robots, and floor mopping robots among cleaning robots) will generate noise during work. The voice signal is picked up by voice, but when the voice signal from the user is picked up, the noise generated during the robot's work is usually picked up by voice, so that the voice signal picked up by the device is mixed with a lot of external noise , the corresponding voice recognition accuracy is not high, which will seriously affect the robot's recognition of external voices (effective signals), and make logical decisions based on voice interpretation (such as performing relevant path planning)

[0003] In the prior art, the method of denoising at the front end of the voice signal is generally to select the appropriate voice signal according to the result of the voice signal classification, and suppress the voice signal that does not meet the requirements, but the method for the voice signal classification is more complicated, not only The noise reduction is not complete, and the efficiency of speech recognition is not high, there are always remaining speech frames that have not been processed, which affects the effect of speech recognition

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