| Title |
Optimization of Speaker Recognition Technology Based on the SACNN-self-attentive Model |
| Authors |
(Guoqiang Lu) ; (Yanmin Bai) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.2.188 |
| Keywords |
SACNN; Self-attentive; Voiceprint recognition; Optimization; Signal denoising |
| Abstract |
The demand for identity recognition and verification accuracy is increasing, presenting new research directions and challenges for enterprises and academic researchers in related fields, the accuracy of voiceprint recognition in practical applications needs improvement, and its results are easily affected by environmental factors and noise, hindering its widespread adoption and application. Traditional voiceprint recognition models often rely on fixed feature extraction and classification methods, which fail to use complex spatiotemporal information in speech signals fully. In addition, existing models do not perform well when dealing with noise and variable acoustic environments, resulting in reduced recognition accuracy. Based on this, this paper uses the SACNN-Self-attentive model to optimize the voicing recognition technology. It uses a wavelet algorithm to pre-process the speech data for noise reduction. Through experiments, the SACNN model converges well in voicing recognition, and the accuracy of the two data sets is 1.12% and 1.24% higher than that of the Deep Speaker. The experimental results show that the model exhibits higher accuracy and stability in various test environments, demonstrating its potential in voice print recognition technology. |