Shot Voice Cloning

Shot voice cloning focuses on synthesizing speech in a new voice using limited training data, aiming to create natural-sounding speech with high speaker similarity. Current research emphasizes zero-shot and few-shot scenarios, employing architectures like transformers, GANs, and recurrent networks, often incorporating multi-modal learning and meta-learning techniques to improve efficiency and performance. This field is significant for its potential to enhance text-to-speech systems, personalize voice assistants, and enable more accessible speech synthesis across multiple languages and speakers, particularly in low-resource settings.