Predictive Packet Loss Concealment Method Based on Conformer and Temporal Convolution Module

To enhance the perceptual quality of speech signal, the Packet Loss Concealment (PLC) technique focuses on recovering the lost speech caused by network latency and jitter. In practical applications, the PLC methods typically employ a predictive process that relies on previously received speech signal to recover the lost speech without introducing additional delay. In this paper, we propose a predictive PLC network, which employs the Conformer and temporal convolution module to fully exploit the contextual dependencies and to better predict the lost speech. In addition, our proposed network can be directly employed as the generator and combined with appropriate discriminative networks, forming a Generative Adversarial Network (GAN) paradigm that can enhance the perceptual quality of the recovered speech signal. Experimental results demonstrate that without any discriminative network, the proposed method demonstrates impressive results in PLC. Under the GAN paradigm, further improvement can be observed and our proposed method outperforms several baseline methods at different packet loss rates.