Marco-Voice Technical Report

arXiv:2508.02038 · cs.CL, cs.SD, eess.AS · Submitted 2026-08-18 · Read on arXiv

Fengping Tian, Chenyang Lyu, Xuanfan Ni, Haoqin Sun, Qingjuan Li, Zhiqiang Qian, Haijun Li, Longyue Wang, Zhao Xu, Weihua Luo, Kaifu Zhang

cs.CL, cs.SD, eess.AS

Submitted: 2026-08-18

Updated: 2026-08-19

Code: https://github.com/AIDC-AI/Marco-Voice

Terminology

• Accuracy — 17× in this paper · explained in 1 episode
• Embeddings — 17× in this paper · explained in 2 episodes
• Contrastive Learning — 10× in this paper · explained in 5 episodes
• Flow Matching — 9× in this paper · explained in 7 episodes
• Emotional Speech Dataset — 8× in this paper
• Attention Mechanism — 4× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Cross-Attention Mechanism — 4× in this paper · explained in 2 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Unified Framework — 3× in this paper · explained in 5 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• EmoSpeech: Guiding FastSpeech2 Towards Emotional Text to Speech
• CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models
• Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
• Cross-speaker Emotion Transfer Based On Prosody Compensation for End-to-End Speech Synthesis
• Spontaneous Style Text-to-Speech Synthesis with Controllable Spontaneous Behaviors Based on Language Models
• CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency Models
• UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal Prompts
• EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark
• DS-TTS: Zero-Shot Speaker Style Adaptation from Voice Clips via Dynamic Dual-Style Feature Modulation
• NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers
• A Survey on Neural Speech Synthesis
• Improving and generalizing flow-based generative models with minibatch optimal transport
• SaMoye: Zero-shot Singing Voice Conversion Model Based on Feature Disentanglement and Enhancement
• A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI

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