The field of speech processing is moving towards developing more robust and resilient models that can handle diverse noise conditions and acoustic perturbations. Recent work has focused on improving the stability of semantic speech tokenizers, which are crucial for downstream speech language models. Researchers are exploring novel architectures and training methods to achieve better token stability and consistency. Another area of interest is the development of more efficient and scalable spoken language models, with some studies investigating the use of syllabic speech tokenization to reduce computational costs. Additionally, there is a growing interest in incorporating non-linear dynamical systems-inspired approaches to model the complexities of speech. Notable papers include: StableToken, which introduces a consensus-driven mechanism to achieve state-of-the-art token stability, and Scaling Spoken Language Models with Syllabic Speech Tokenization, which demonstrates the potential of syllable-level language modeling for efficient long-context spoken language models. Optimizing Speech Language Models for Acoustic Consistency also presents a notable approach to achieving robust and consistent generation through semantic initialization and planning losses. NLDSI-BWE presents a novel approach to speech bandwidth extension using non-linear dynamical systems-inspired discriminators.