# Speaker recognition Based on Dunge Beetle Optimized CNN
## 1 Introduction  
As an inherent biological attribute of human beings, voiceprint can be used to assist in identity verification. Speaker recognition developed from voiceprint has become an indispensable technology in the financial industry and private devices. [PERSON] et al. applied speaker recognition technology to mobile devices [1]. The main voiceprint feature extraction methods include the Linear Prediction Coding [2], the Linear Prediction Cepstral Coefficients (LPCC) [3, 4], and the Mel-Frequency Cepstral Coefficients (MFCC) [5, 6]. Based on the above methods, a large number of mature recognition models and applications have been proposed. [PERSON] et al. proposed a text-independent speaker recognition method based on the Hidden Markov models (HMM) and the Gaussian mixture model (GMM). They evaluated the robustness of the model affected by speech style [7]. [PERSON] et al. proposed a method to combine the speaker and the noise source into a noisy speaker HMM with a specific signal-to-noise ratio (SNR) and use this likelihood value to obtain the recognition result [8]. [PERSON] et al. proposed to compare the recognition rates of multiple combination models using vector quantization and dynamic time warping, and the results showed that LPCC and MFCC had better performance [9]. [PERSON] et al. proposed the GMM Universal Background Model, which utilized the speaker's trained speech and a Bayesian adaptive form to adjust the parameters of UBM [10]. The structural constraint is a major drawback of the above models, which may not be sufficient to model tasks when faced with complex tasks [11]. Non-parametric models have better flexibility and performance. [PERSON] et al. introduced a new technology for precise indexing, which was a model based on template matching, which solved the problem of different pronunciation lengths, but it could onlybe used in isolated word speech recognition [12]. [PERSON] et al. proposed the use of GMM supervectors in a support vector machine (SVM) classifier and produced excellent classification accuracy in speaker recognition [13]. However, the performance of the above models is highly susceptible to environmental noise, often resulting in a sharp drop in recognition rate. CNN has been widely used in the fields of image classification and object detection due to its excellent feature extraction ability. In speaker recognition, CNN achieved good results in classifying spectrograms, with significantly improved anti-noise performance [14; 15]. [PERSON] et al. proposed a hybrid recognition method based on CNN and MFCC, which achieved a recognition accuracy of 87.5% [16]. [PERSON] et al. proposed a new model to improve the recognition accuracy of short speech speaker recognition systems by addressing the issue of GMM being unable to recognize short speech speakers accurately and reducing the recognition error rate from 4.9% to 2.5% [17]. [PERSON] et al. created the VoxCeleb2 dataset and used a deep CNN to classify it with 92.67% accuracy [18]. [PERSON] et al. described a text-independent CNN model for speaker recognition and achieved recognition rates from 75% to 85% [19]. [PERSON] et al. proposed a voiceprint recognition model based on Mel time-spectrum convolutional neural network for identifying faults in transformers during operation. This method constructed a CNN model by feature extraction preprocessing and Mel filter. This model could recognize the voiceprint of transformers by four different operating faults [20]. However, the hyperparameters of these models are mostly manually tuned, which requires experience and skill; thus, obtaining the highest-performing CNN models is time-consuming [21]. Sometimes, the hyperparameters set based on experience will make the model fall into a local optimal solution or even fail to converge. Recently, many scholars have devoted themselves to efficient optimization algorithms to tune the hyperparameters of CNNs.