Advancements in Zero Shot Voice Cloning using...

Advancements in Zero Shot Voice Cloning using Large Speech Models

Author Name : Aatish kumar Dhami, Er. Niharika Singh

ABSTRACT Recent developments in zero-shot voice cloning have demonstrated significant progress by leveraging large-scale speech models. These advancements enable the synthesis of high-fidelity, natural-sounding speech from limited input data, often as little as a few seconds of voice recording, without requiring speaker-specific fine-tuning. By employing robust architectures trained on diverse, multilingual datasets, these models capture the nuanced characteristics of individual voices, facilitating rapid adaptation to new speakers in real-world applications. This paper reviews the state-of-the-art methodologies, highlights the integration of deep learning techniques with transformer architectures, and discusses challenges related to model generalizability, ethical considerations, and computational efficiency. The implications of these advancements suggest promising avenues for personalized communication technologies, improved accessibility tools, and enhanced multimedia content generation, marking a pivotal step forward in the field of speech synthesis and voice cloning.