# A Deep Neural Network Model for Speaker Identification
###### Abstract
We compare its performance with some existing network models for the speaker identification task, including the Mel frequency cepestral cofficient-Gaussian mixture model(MFCC-GMM) model proposed by [PERSON] et al. [10], the CNN model proposed by [PERSON] et al. [8], the RNN model proposed by [PERSON] et al. [9], and the multimodal Long Short-Term Memory (LSTM) model proposed by [PERSON] [11]. In the process of proposing this model, we also tested other network models. The compared results among models are described in the results section of the paper. Moreover, our proposed network models for speaker identification were evaluated on the publicly available Aishell-1 datasets [12]. The main contribution of this paper is to propose a DNN model that combines the feature extraction advantages of CNN and the feature learning ability of GRU for time series information for speaker identification task. The reasons for CNN and GRU were chosen for model construction are as follows: 1. CNN is effective for spatial feature extraction and the spectrogram of speech contains spatial features of voiceprint, we chose CNN layers to extract features from the spectrogram. 2. Since deep networks can better represent long utterances than shallow networks and GRU has an excellent feature learning ability for time series information, and GRU has a better performance than traditional RNN, we chose stacked GRU layers in our model for frame-level feature extraction. Furthermore, it can be concluded that this method that combines the characteristics of CNN and RNN models could be effective on research similar to the study of speaker identification. Moreover, we compare its performance with existing network models for a speaker identification task and demonstrate the effectiveness of the proposed DNN model. The rest of this paper is organized as follows. Related Works describes existing works on speaker identification. Section 2 presents the materials and methods of the proposed deep GRU network model, which mainly includes the following modules. A background overview of RNN and CNN are discussed in the background subsection. The proposed model is shown in the model overview subsection. The details of the dataset and data preprocessing are reported in the dataset and data preprocessing subsection. Section 3 presents the experimental results, with a description of the training process and evaluation metrics, and a comparison of several network models. Section 4 presents the conclusion of the study and describes the future work outlook. Related WorkResearch on speaker identification has a long history. In the 1940s, researchers began the study of speaker identification by conducting research on the spectrogram, but the effect was unsatisfying. In 1956, several computer scientists put forward the concept of artificial intelligence [13], then speaker recognition began to enter the era of artificial intelligence research. At that time, researchers hoped to realize the identification of speakers through computer programs, but due to poor computer hardware capabilities and the immaturity of related algorithms, research on speaker identification did not achieve great results. Until the 1980s, as a powerful branch in the field of artificial intelligence, machine learning [14] research began to use algorithms to analyze data, obtain relevant feature information from it, and then make decisions and predictions to solve the problem. Unlike traditional software programs designed to solve specific tasks, machine learning is trained with a large amount of data, using various algorithms to learn how to complete tasks from the data. The research on speaker identification has been greatly developed. The speaker identification process during this period usually consists of the following stages: Speech data preprocessing, feature extraction, speaker modeling, and scoring. Speech preprocessing includes pre-emphasis, framing, windowing, etc.