## 1 Introduction  
Currently, most of the calls in call centers are recorded, in many cases just because it is mandatory with the current legislation. Besides legal requirements, these recordings constitute a rich source of information about users, the call center operators, the efficiency of the campaigns, and market trends, which can be translated into valuable information on the business. In call centers, hundreds or thousands of hours are recorded daily, so they are typically stored in a highly compressed way to save space. An undesired consequence is that these recordings are normally stored with very limited quality. Voice-over-IP (VoIP), the transmission of speech over IP packets, is nowadays mainstream in call centers and their recording systems. VoIP can make use of different speech codecs, and depending on the selected speech codec, the length of the packet used for the transmission of the speech signals can change. Normally, the packet length is between 20 and 40 ms. Given that IP-based networks do not provide secure transmission in real time, packets can be lost or delayed. If the delay is small, the receiver can wait a small amount of time by introducing some latency to give some time for the packet to arrive. However, the latency has to be limited to allow a natural human-to-human communication. Consequently, a packet that is delayed more than a threshold is considered lost, and speech needs to be reconstructed without that package, which obviously introduces important degradation in the reconstructed speech. This degradation is added to the degradation introduced by speech coding and other common problems such as echoes, noise, etc. in VoIP [1]. Packet Loss Concealment (PLC) techniques have been introduced to mitigate losses or delay issues and are crucial in VoIP systems. These techniques can vary from the most basic forms such as filling the lost packets by repeating the last frame or adding noise to fill the lost frame, to some more complex forms such as using interpolation methods to reconstruct the signal [2; 3]. Given the importance of PLC in VoIP, some speech codec standards have included PLC techniques, such as ITU-T G.711 Appendix I [4], ITU-T G.729 [5] or ITU-T G.722 in Appendix III and Appendix IV [6]. All these methods have two limitations in common: the decoder needs to know when a packet is lost to be capable to mitigate it, and the lost packet can not be in the first frames because mitigation is based on the last packets received. Different methods have been proposed in the field of Packet Loss Concealment. The first techniques were waveform level approaches, such as those included in some of the VoIP codecs and works such as [7; 8; 9]. Later, statistical models such as Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) have been proposed as possible solutions to the problem of PLC. These statistical models provide a more complex and richer description of the speech signal (provided that enough speech material is available for training), which results in better PLC results [10; 11; 12]. In the last few years, with the advent of the era of deep learning, the use of Deep Neural Networks (DNN) has become common in many aspects of speech processing. Deep learning techniques have proved to be able to extract higher level features from less processed data and to be capable of modeling, predicting, and generalizing better than classical (statistical) techniques. PLC has not been an exception, and several approaches have been proposed in the last few years for deep learning PLC based on regression either in waveform or time-frequency domains [13; 14; 15; 16; 17], based on autoencoders [18; 19] and based on Generative Adversarial Networks (GANs) [20; 21; 22; 23]. The field of Automatic Speech Recognition (ASR) has also benefited from deep learning approaches in the last few years, and currently all state-of-the-art systems include deep neural networks either combined with HMM in a hybrid Hidden Markov Model-Deep Neural Network (HMM-DNN) system [24] or as an end-to-end neural model [25].