The recent advancements in speech processing and synthesis have seen a shift towards more context-aware and versatile models. Innovations in zero-shot voice conversion, such as the introduction of diffusion transformers and external timbre shifters, have significantly improved speaker similarity and reduced word error rates. Additionally, the integration of fundamental frequency conditioning has extended these capabilities to singing voice conversion. In the realm of word boundary detection, supervised frame classification methods have set new benchmarks, particularly with the use of pre-trained models like HuBERT, enhancing the accuracy of speech segmentation. Neural vocoders, such as the proposed ESTVocoder, have advanced by incorporating excitation-spectral transformations, leading to higher speech quality and faster convergence. The field has also seen progress in dynamic scene-based noise addition through generative models, which offer more realistic and adaptable acoustic environments. Furthermore, data augmentation techniques for ASR have evolved to leverage large language models and zero-shot TTS, significantly reducing word error rates and enhancing data efficiency. Lastly, multi-modal TTS approaches, such as I2TTS, have introduced spatial perception capabilities, enabling more immersive and context-specific speech synthesis.

Noteworthy papers include one on zero-shot voice conversion that introduces a novel framework with diffusion transformers and timbre shifters, and another on multi-modal TTS that integrates visual scene prompts for enhanced spatial perception.