Cross-Domain Knowledge Transfer in Speech Processing: A Study on Multilingual and Multimodal Speech Synthesis Models

Abstract

This paper explores the concept of cross-domain knowledge transfer in speech processing, with a focus on multilingual and multimodal speech synthesis models. The increasing demand for versatile and adaptive speech synthesis systems necessitates the exploration of methods that enable models to leverage knowledge from different domains, languages, and modalities. We investigate various approaches to transfer learning in speech synthesis, analyze their effectiveness in multilingual and multimodal contexts, and propose novel techniques to enhance cross-domain knowledge transfer. Our findings highlight the potential for improving speech synthesis performance across diverse languages and modalities by integrating and transferring knowledge from related tasks and domains.