Guo, X.; Qin, X.; Zhang, Q.; Zhang, Y.; Wang, P.; Fan, Z. Speaker Recognition Based on Dung Beetle Optimized CNN. Appl. Sci.2023, 13, 9787.
Guo, X.; Qin, X.; Zhang, Q.; Zhang, Y.; Wang, P.; Fan, Z. Speaker Recognition Based on Dung Beetle Optimized CNN. Appl. Sci. 2023, 13, 9787.
Guo, X.; Qin, X.; Zhang, Q.; Zhang, Y.; Wang, P.; Fan, Z. Speaker Recognition Based on Dung Beetle Optimized CNN. Appl. Sci.2023, 13, 9787.
Guo, X.; Qin, X.; Zhang, Q.; Zhang, Y.; Wang, P.; Fan, Z. Speaker Recognition Based on Dung Beetle Optimized CNN. Appl. Sci. 2023, 13, 9787.
Abstract
Speaker recognition methods based on convolutional neural networks (CNN) have been widely used in the security field and smart wearable devices. However, the traditional CNN has a large number of hyperparameters that are difficult to be determined, which makes the model easy to fall into local optimum or even fail to converge during the training process. Intelligent algorithms such as particle swarm optimization and genetic algorithm are used to solve the above problems. However, these algorithms have poor performance compared with the current emerging meta-heuristic algorithms. In this study, the dung beetle optimized convolution neural network (DBO-CNN) is proposed to identify the speakers, which is helpful in finding suitable hyperparameters for training. By testing the dataset of 50 people, it was demonstrated that the accuracy of the model was significantly improved by using this approach. Compared with the traditional CNN and CNN optimized by other intelligent algorithms, the accuracy of DBO-CNN has increased by 0.6%~4.8%, and reached 98.3%.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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