# VECL-TTS: Voice identity and Emotional style controllable Cross-Lingual Text-to-Speech

arXiv: https://arxiv.org/abs/2406.08076 · HTML: https://mlsanity.com/paper/2406.08076/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [Beyond Global Emotion: Fine-Grained Emotional Speech Synthesis with Dynamic Word-Level Modulation](https://mlsanity.com/paper/2509.20378/) (2025)
- [EmoReg: Directional Latent Vector Modeling for Emotional Intensity Regularization in Diffusion-based Voice Conversion](https://mlsanity.com/paper/2412.20359/) (2024)
- [Optimizing Multilingual Text-To-Speech with Accents & Emotions](https://mlsanity.com/paper/2506.16310/) (2025)
- [Adaptive Oscillatory Inductive Bias for Modeling Sharp Prosodic Dynamics in Diffusion-Based TTS](https://mlsanity.com/paper/2606.25424/) (2026)

## Abstract (arXiv)

Despite the significant advancements in Text-to-Speech (TTS) systems, their full utilization in automatic dubbing remains limited. This task necessitates the extraction of voice identity and emotional style from a reference speech in a source language and subsequently transferring them to a target language using cross-lingual TTS techniques. While previous approaches have mainly concentrated on controlling voice identity within the cross-lingual TTS framework, there has been limited work on incorporating emotion and voice identity together. To this end, we introduce an end-to-end Voice Identity and Emotional Style Controllable Cross-Lingual (VECL) TTS system using multilingual speakers and an emotion embedding network. Moreover, we introduce content and style consistency losses to enhance the quality of synthesized speech further. The proposed system achieved an average relative improvement of 8.83% compared to the state-of-the-art (SOTA) methods on a database comprising English and three Indian languages (Hindi, Telugu, and Marathi).