The field of speech processing and synthesis is moving towards more robust and efficient models, with a focus on self-supervised learning, adaptive noise resilience, and improved evaluation benchmarks. Notably, researchers are exploring new ways to optimize speech processing tasks, such as target-speaker speech processing, and developing more effective methods for evaluating the performance of these models. Additionally, there is a growing interest in developing lightweight and efficient models for speech synthesis and keyword spotting, which can be deployed on resource-constrained devices. Some notable papers in this area include: The TS-SUPERB benchmark, which provides a comprehensive evaluation framework for target-speaker speech processing tasks. The Lightweight End-to-end Text-to-speech Synthesis model, which achieves state-of-the-art performance while requiring minimal computational resources. The SpecWav-Attack model, which leverages spectrogram resizing and Wav2Vec 2.0 for attacking anonymized speech. The Adaptive Noise Resilient Keyword Spotting method, which enables dynamic adaptation and improves noise robustness in keyword spotting systems.