The field of audio processing and speech technology is rapidly evolving, with a focus on improving the accuracy and efficiency of various tasks such as speech enhancement, voice conversion, and speech recognition. Researchers are exploring new approaches, including the use of deep learning frameworks, diffusion-based models, and multi-scale hybrid attention networks, to address challenges in these areas. One notable trend is the development of methods that can effectively utilize prior knowledge and external guidance to enhance audio processing systems. For example, the introduction of Gaussian priors and deterministic enhanced conditions has been shown to improve the performance of speech enhancement and voice conversion systems. Additionally, the use of unit language and prosody-aware audio codecs is being explored to advance speech modeling and voice conversion capabilities. Noteworthy papers in this area include PAST, which proposes a novel end-to-end framework for phonetic-acoustic speech tokenization, and Neurodyne, which introduces a neural pitch manipulation system with representation learning and cycle-consistency GAN. Overall, these advances have the potential to significantly improve the performance and versatility of audio processing and speech technology systems.