The field of speech synthesis and editing is witnessing significant advancements with the emergence of innovative approaches that prioritize efficiency, quality, and control. Recent developments indicate a shift towards diffusion-based models, state space models, and cross-modal denoising techniques, which are being explored for their potential in improving speech synthesis, style transfer, and audio-visual editing. These methods aim to address long-standing challenges such as synchronization, coherence, and nuance, offering more natural and expressive outcomes. Notably, the integration of textual style descriptors with acoustic attributes is becoming a focal point, enabling finer control over speaker characteristics, prosody, and timbre. Noteworthy papers include: WaveFM, which presents a reparameterized flow matching model for high-fidelity speech synthesis. ReverBERT, which introduces an efficient framework for text-driven speech style transfer leveraging a state space model paradigm. Text-Driven Voice Conversion via Latent State-Space Modeling, which proposes a novel approach for text-driven voice conversion using latent state-space modeling.