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This document discusses the design of diffusion-based neural speech codecs (NSCs) and presents a systematic analysis of their performance compared to traditional codecs and generative adversarial networks (GANs). The authors propose a categorization framework based on conditioning and output domains, explore new designs, and evaluate the models against existing baselines. The findings indicate that diffusion models have not been fully explored in the context of NSCs, despite their potential advantages in au

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