## 1 Introduction
The reconstruction-based PLC uses the knowledge of speech coding to reconstruct the codec parameters, which can synthesize the speech in the lost packet, so it has better PLC performance. For example, in the ITU-T G.723.1 speech coding standard [16], the decoder interpolates the state of linear predictor coefficients based on the results of the unvoiced and voiced signal judgment in the lost packet, and decides whether to use periodic excitation of the previous packet or generate the matched random signal. In addition, some nonlinear models, such as hidden Markov model [17] and Gaussian mixture model [18], were also utilized to predict codec parameters of the lost packet to reconstruct the speech signal of the lost packet. In recent years, with the continuous learning and research of the DNN, the DNN has played a mainstay role in solving complex problems in various fields. The DNN-based PLC method has the following advantages: DNN has strong modeling ability, which has been widely used in the fields of speech activity detection, speech enhancement, and speech synthesis. Especially, the log-power spectrum (LPS), as a core feature of speech signal, has been successfully applied in many speech processing tools based on the DNN because of its small dynamic range, obvious formant, and harmonic structure [19; 20]. Due to strong linear correlation between the adjacent frames of speech signal [21], DNN can learn the mapping relationship of the LPS of the speech signal from the correct packet to the lost packet [21; 22], so as to achieve the purpose of recovering the lost speech. For example, two DNNs were used to separately predict the amplitude spectrum and phase spectrum of the lost packet through historical data packets for the PLC in [21]. In [23], a generative adversarial network (GAN) was employed to predict the waveform of the lost packet in time domain through speech waveform before the lost packet. In [24], a recurrent neural network (RNN) was utilized to predict the lost speech signal by learning historical information, which can directly generate speech samples of the lost packet in the time domain. However, the estimation of the phase spectrum using the DNN often causes large errors. Therefore, in response to this problem in the packet loss scenario, in this paper, a time domain phase correction method and a frequency domain phase correction method are proposed to achieve packet loss concealment combined with the DNN. Firstly, the PLC method based on WSOLA and DNN is used to complete the lack of information in data packets in the case of poor conditions. In this method, a stretching factor is set to stretch the signal waveform, and the information of the lost part of the speech signal is covered up in the time domain, so that it can complete the purpose of concealing the loss of speech phase information while maintaining unchanged pitch period. At the same time, combined with the prediction and estimation of the amplitude spectrum of packet loss speech by the DNN, the speech amplitude information loss concealment is completed. In the second method, the GLA [25; 26; 27] and DNN are used to realize the PLC. In this method, the DNN is used to estimate amplitude and phase spectra of the speech signal in the lost packet. In order to improve phase spectrum estimation, the GLA is used to correct phase spectrum in the frequency domain through the amplitude spectrum so that the phase spectrum can match the amplitude spectrum. The structure of the paper is organized as follows: the packet loss model is described in Section 2. The proposed PLC methods based on phase correction and DNN are given in Section 3. Experiments and discussion are shown in Section 4. Finally, conclusions and future work are summarized in Section 5.