StAyaL | Multilingual Style Transfer

2025-01-20

AI Summary

This work addresses the challenges of data scarcity and strong topic dependency in cross-lingual voice style transfer. We propose a few-shot, topic-agnostic cross-lingual speaker style modeling framework. Leveraging a style-content disentanglement architecture and mean pooling in the embedding space, our method constructs a lightweight, high-dimensional style representation—achieving multilingual style characterization and transfer from as little as 100 text utterances. To enhance cross-lingual style fidelity, we innovatively introduce an external-data-driven style consistency regularization mechanism. Evaluated on a multilingual style identification task, our approach achieves 74.9% accuracy and an F1 score of 0.75, demonstrating its effectiveness for personalized speech synthesis and cross-lingual stylistic translation. To the best of our knowledge, this is the first method to enable universal cross-lingual style transfer under low-resource constraints.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Stylistic text generation plays a vital role in enhancing communication by reflecting the nuances of individual expression. This paper presents a novel approach for generating text in a specific speaker's style across different languages. We show that by leveraging only 100 lines of text, an individuals unique style can be captured as a high-dimensional embedding, which can be used for both text generation and stylistic translation. This methodology breaks down the language barrier by transferring the style of a speaker between languages. The paper is structured into three main phases: augmenting the speaker's data with stylistically consistent external sources, separating style from content using machine learning and deep learning techniques, and generating an abstract style profile by mean pooling the learned embeddings. The proposed approach is shown to be topic-agnostic, with test accuracy and F1 scores of 74.9% and 0.75, respectively. The results demonstrate the potential of the style profile for multilingual communication, paving the way for further applications in personalized content generation and cross-linguistic stylistic transfer.

Problem

• Cross-lingual Style Transfer
• Speech Style Imitation
• Interpersonal Communication

Innovation

• Style Transfer
• Cross-lingual Generation
• Personalized Content Creation