Neural End 2 End Speech

Neural end-to-end speech coding aims to replace traditional speech codecs with deep learning models for efficient and high-quality speech transmission. Current research focuses on improving the robustness of these neural codecs to real-world challenges like packet loss and noise, often employing architectures like convolutional recurrent neural networks (RNNs) and generative adversarial networks (GANs) alongside techniques such as packet loss concealment and forward error correction. This approach promises significant improvements in speech communication quality and efficiency for applications such as VoIP and real-time communication systems, particularly at low bitrates. The integration of speech enhancement capabilities within the end-to-end framework further enhances the overall system performance.