An adaptive algorithm for voice quality based on big data voiceprint identification

Abstract

This paper improves the speech amplitude in Bayesian speech enhancement estimation by introducing a super-Gaussian cardinality distribution probability density function in the filter's construction. The derivation is combined with the perceptual error function, the new probability density function and the perceptual error cost function to better exploit and utilize the prior statistical information of the speech. The results show that the proposed method can improve the signal-to-noise ratio up to 0.7 dB under different noises and different signal-to-noise ratios, and the processed speech has better feasibility, which provides good speech enhancement for the processing of noisy speech quality in vocal identification practice without significantly increasing the computational complexity and can be better adapted to the application.

Keywords

• vocal identification
• noise sound quality
• bayesian algorithm
• speech enhancement
• gaussian cardinality distribution
• 97p10