The recent advancements in speech processing and voice conversion technologies are pushing the boundaries of what is possible in the field. Innovations are being driven by the integration of diffusion models, neural codecs, and self-supervised learning techniques, which are enabling more robust, high-fidelity, and interpretable systems. Notably, there is a strong emphasis on developing models that can operate in zero-shot or one-shot scenarios, allowing for greater flexibility and applicability in real-world conditions. These models are not only improving the quality of voice conversion and speech synthesis but also enhancing the interpretability and explainability of the underlying processes, which is crucial for clinical and diagnostic applications. Additionally, the field is witnessing a shift towards more efficient and disentangled neural codecs that can handle complex speech information with fewer tokens, thereby advancing the state-of-the-art in speech coding and synthesis. The integration of these advanced techniques is paving the way for more natural, controllable, and diverse speech styles in text-to-speech systems, as well as more accurate and user-friendly automatic severity classification in dysarthric speech. Furthermore, the purification of speech representations for end-to-end speech translation is addressing the limitations posed by irrelevant speech factors, leading to significant improvements in translation performance.