The recent advancements in the field of audio and audiovisual deepfake detection and synthetic speech analysis have shown a significant shift towards interdisciplinary and transdisciplinary approaches. Researchers are increasingly integrating linguistic knowledge with AI methods to enhance the robustness and comprehensiveness of deepfake detection systems. This trend is evident in the development of more sophisticated models that can analyze both audio and visual streams simultaneously, improving detection accuracy and addressing the limitations of unimodal approaches. Additionally, there is a growing emphasis on creating realistic training datasets for AI-based speaker separation systems, which is crucial for enhancing model performance in real-world scenarios. The field is also witnessing innovative solutions for multilingual communication in disaster situations, leveraging AI and blockchain technology to ensure secure and efficient information dissemination. Notably, the robustness of AI-synthesized speech detection methods is being advanced through feature decomposition learning and synthesizer feature augmentation, aiming to improve performance across various synthesizers. Overall, the research landscape is evolving towards more integrated, robust, and context-aware solutions that address the multifaceted challenges posed by deepfakes and synthetic speech.