# A Deep Neural Network Model for Speaker Identification
###### Abstract
Feature extraction is to extract the characteristic parameters that effectively characterize the speaker's traits from the speech signals. The typical features include mel-frequency cepstral coefficients [15], linear prediction coefficients [16], linear prediction cepstral coefficients [17], etc. Then speaker models are built through an analysis of these extracted features. At present, many methods are still used for speaker modeling such as gaussian mixture models (GMM) [5], GMM-universal background model [18], hidden Markov model [19], and the neural network model. The DNN model is discussed in this study. In the past 10 years with the development of deep learning [20; 21],CNN has been used in speaker identification tasks. In a neural network model, when given labeled data, a speaker recognition system based on deep learning extracts each speaker's deep features and performs supervised learning. [PERSON] designed a convolutional neural network [22] which obtained an accuracy of 97.0% on the TIMIT dataset, corresponding to 19 misidentified speakers on a whole set of 630 speakers [22]. At the same time, LSTM networks [23] and traditional RNN [24] have been successfully applied to various sequence prediction and sequence labeling tasks. In language modeling, a traditional RNN model has obtained a significant reduction in perplexity over standard n-gram models [25]. By designing some gate structures, the LSTM network solves the long-term dependencies problem in traditional RNN and alleviates the vanishing gradient problem. Currently, the LSTM network has outperformed traditional RNN in language identification tasks [26]. LSTM models also perform better than traditional RNN models on learning context-free and context-sensitive languages [27]. For online and offline handwriting recognition, LSTM networks used together with a connectionist temporal classification (CTC) layer and trained from unsegmented sequence data, have been shown to outperform the hidden Markov model (HMM)-based system [28]. Similar techniques with a deep LSTM network have been proposed to perform grapheme-based speech recognition [29]. LSTM networks have also been proposed for phoneme prediction in a multi-stream framework for continuous conversational speech recognition [30]. However, the number of parameters for the LSTM network training is four times than traditional RNN, which is easy to cause overfitting. By further optimizing the structure, GRU has become a widely-used variant of RNN. The GRU network is similar to the LSTM network in performance, but the former network is faster to train and less likely to diverge. In current deep learning solutions, LSTM and GRU are often applied to tasks such as visual localization [31], traffic noise prediction [32], stock market forecast [33], and air pollution forecasting [34]. As mentioned above, since the speech signal is a continuous one-dimensional time series, most of the current research methods are based on CNN or RNN. Indeed, these networks perform well on some datasets [35], but there is no attempt to combine these two networks to study the speaker identification task. Due to the spectrogram that a speech signal contains, the spatial features of voiceprint and CNN are effective for spatial feature extraction and modeling spectral correlations in acoustic features [36], thus we chose CNN to extract features from the spectrogram. On the other hand, deep networks can better represent long utterances than shallow networks [37], thus stacked GRU layers are used in the model for frame-level feature extraction, since they have been proven to be effective for speech processing [38; 39]. The proposed model combining GRU and CNN should contain a better result.