Machine Learning-Based Source-Matched Channel Coding for Speech Transmission

This paper proposes a novel machine learning-based source-matched channel coding approach for transmission of short speech frames. We use a state-of-the-art speech codec to compare conventional channel coded transmission, uncoded transmission, and the proposed scheme. Our results demonstrate that our separate source and channel coding approach for short frames achieves superior performance compared to a state-of-the-art joint source and channel coding (JSCC) approach. By keeping separated source and channel coding, we take a step towards addressing network-related aspects, and we allow for independent training of the source codec and thus reduce the training complexity of the overall transmission system. Additionally, we present results on peak-to-average power ratio (PAPR) constrained transmission to facilitate the implementation of the proposed approach in real-world applications.