# Speaker recognition Based on Dunge Beetle Optimized CNN
## 1 Introduction
Swarm-based intelligent algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) were used to optimize the hyperparameters of CNN [22; 23; 24; 25]. [PERSON] et al. proposed a method of optimizing CNN with GA and tested image recognition on the MNIST dataset and achieved an accuracy rate of 99.4% [22]. [PERSON] et al. used GA to adjust the hyperparameters of CNN and achieved an accuracy rate of 95.5% in the emotion recognition test, which had a great advantage over other methods [23]. [PERSON] et al. proposed a PSO-optimized adaptive CNN(PSO-CNN) to analyze the spectrogram during the working process of the bearing to determine whether the bearing was damaged. This method had a recognition accuracy rate of 99.9% for the four damage situations [24]. [PERSON] et al. used the dragonfly optimizer based on information gain and the CNN classifier optimized by particle PSO based on depth clustering to identify network attack aircraft types. The optimization algorithm could reduce clustering losses and network losses [25]. The above studies showed that swarm-based optimization methods were effective in improving the performance of CNN. However, after decades of technological development, the above intelligent algorithms have shown limited effectiveness in current complex and huge engineering problems. Researchers have proposed many heuristic search algorithms inspired by nature that imitate various biological habits. These bionics-based strategies have the outstanding features of high optimization power, high fast convergence, and excellent robustness and can be applied to optimize the hyperparameters of the network. The bionics-based algorithm has two core strategies for biological populations: role division and behavioral differentiation. Role division refers to the use of fitness to divide the population into different groups, while behavioral difference means that individuals in different groups have different action strategies. In population, the role played by each individual is not static. During the foraging process, the fitness of individuals will change, which will change their role. However, no matter how the population is divided, the ultimate goal of all individuals is the same, that is, to find the most suitable habitat for the survival and reproduction of the population. In particular, the action strategies of individuals in bionic algorithms are more reasonable and effective because the objects they imitate are multiplying and thriving in real life. In recent years, the Whale Algorithm [26], Grey Wolf Optimization [27], Sparrow Search Algorithm (SSA) [28], and Jumping Spider Optimization Algorithm [29] were heuristic algorithms proposed based on the above ideas, which were efficient search capabilities. They have been applied in many fields [30; 31; 32; 33]. Based on the above analysis, the DBO is proposed to optimize the hyperparameters of CNN to improve the correct rate of speaker recognition and its anti-noise ability. DBO is the latest heuristic algorithm proposed by [PERSON] et al., with excellent exploration and high local optimal avoidance ability [34]. In this algorithm, the stratum distribution and foraging habits in the dung beetle group are simulated, and the different foraging strategies to find the optimal solution within a certain range are used. In addition, a comparison between the DBO and the other algorithms is implemented, and the experimental results demonstrate the superiority of DBO in optimizing CNN hyperparameters. This is the first time DBO has been used for speaker recognition. The paper is structured as follows: Sec. II shows the processing flow of audio data. Sec. III introduces the CNN model architecture and DBO calculation process. Sec. IV shows the result and discussion about optimized CNN. Sec. V presents conclusions and future work.