The field of speaker diarization and code-switching speech recognition is moving towards more accurate and robust models, with a focus on handling diverse languages, accents, and speaking styles. Recent research has highlighted the importance of domain adaptation, error analysis, and evaluation frameworks in improving the performance of speaker diarization systems. The development of new benchmarks and datasets has also enabled more comprehensive evaluations of multilingual ASR models. Notably, innovative approaches such as simulated data augmentation and hierarchical evaluation frameworks have shown promising results in advancing the field. Noteworthy papers include: Domain-Aware Speaker Diarization On African-Accented English, which proposes a lightweight domain adaptation approach to reduce errors in speaker diarization. HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition, which introduces a new evaluation framework for code-switching speech recognition. SAGE-LD: Towards Scalable and Generalizable End-to-End Language Diarization via Simulated Data Augmentation, which presents a neural spoken language diarization model that supports an unconstrained span of languages within a single framework.