# A Deep Neural Network Model for Speaker Identification
###### Abstract  
Speaker identification is a classification task which aims to identify a subject from a given time-series sequential data. Since the speech signal is a continuous one-dimensional time series, most of the current research methods are based on convolutional neural network (CNN) or recurrent neural network (RNN). Indeed, these methods perform well in many tasks, but there is no attempt to combine these two network models to study the speaker identification task. Due to the spectrogram that a speech signal contains, the spatial features of voiceprint (which corresponds to the voice spectrum) and CNN are effective for spatial feature extraction (which corresponds to modeling spectral correlations in acoustic features). At the same time, the speech signal is in a time series, and deep RNN can better represent long utterances than shallow networks. Considering the advantage of gated recurrent unit (GRU) (compared with traditional RNN) in the segmentation of sequence data, we decide to use stacked GRU layers in our model for frame-level feature extraction. In this paper, we propose a deep neural network (DNN) model based on a two-dimensional convolutional neural network (2-D CNN) and gated recurrent unit (GRU) for speaker identification. In the network model design, the convolutional layer is used for voiceprint feature extraction and reduces dimensionality in both the time and frequency domains, allowing for faster GRU layer computation. In addition, the stacked GRU recurrent network layers can learn a speaker's acoustic features. During this research, we tried to use various neural network structures, including 2-D CNN, deep RNN, and deep LSTM. The above network models were evaluated on the Aishell-1 speech dataset. The experimental results showed that our proposed DNN model, which we call deep GRU, achieved a high recognition accuracy of 98.96%. At the same time, the results also demonstrate the effectiveness of the proposed deep GRU network model versus other models for speaker identification. Through further optimization, this method could be applied to other research similar to the study of speaker identification. speaker identification; speaker recognition; recurrent neural network; spectrogram; two-dimensional convolutional neural network; gated recurrent unit; mel-filterbank energy features; aishell-1 +
Footnote †: journal: Medical  
latter aims at identifying the speaker. In this paper, we mainly conduct research on speaker identification. Speaker identification is the task of determining an unknown speaker's identity by their speech signal, it is a 1:N match processing in which the extracted feature is compared against multiple templates [5]. In the speaker identification process [6], extracting speaker features from speech signals is a pivotal task. Human speech signals are a powerful medium of communication that contain rich information, such as gender, emotional traits, accent, etc. These unique characteristics enable researchers to identify speakers by voiceprint recognition [7]. The collected speaker utterances are fed into the deep learning network for training. In the recognition process, the speaker identification system matches the extracted speaker features with those in the model library. Then, the speaker with the highest probability of utterance is identified as the target speaker. However, the stability of speaker identification is not enough. The recognition depends on the length of the voice, the voice collection environment, and the physical condition of the speaker. In addition, the recognition performance is generally not satisfactory in a noisy environment. Convolutional neural network (CNN) or recurrent neural network (RNN) models have performed well [8; 9] in some speaker identification tasks, but we anticipate that the fusion of CNN and RNN models can benefit from different advantages due to their contrasting architectures. In this study, we propose a deep neural network (DNN) model based on CNN and gated recurrent unit (GRU) for the speaker identification.