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End-to-end training unifies TTS components for better speech generation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel end-to-end training framework for discrete token Large Language Model (LLM) based Text-to-Speech (TTS) systems. This approach unifies the training of the speech tokenizer, LLM, a flow-matching model, and a reward model, unlike previous cascaded systems trained independently. The joint optimization encourages the discrete speech token space to better capture acoustic and semantic information, leading to improved TTS generation. Experiments show this end-to-end method achieves state-of-the-art results on the Seed-TTS-Eval benchmark with a significantly smaller LLM. AI

IMPACT This unified training approach could lead to more efficient and higher-quality speech synthesis models.

RANK_REASON The cluster contains an academic paper detailing a new methodology for training TTS systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

End-to-end training unifies TTS components for better speech generation

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The cluster contains an academic paper detailing a new methodology for training TTS systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Changfeng Gao, Yong Ren, Jun Yuan, Ye Bai, Zhao You, ShiDong Shang ·

    End-to-End Training for Discrete Token LLM based TTS System

    arXiv:2606.09234v1 Announce Type: cross Abstract: Recent state-of-the-art (SOTA) text-to-speech (TTS) systems typically adopt a cascaded pipeline consisting of a speech tokenizer, an autoregressive large language model (LLM), and a diffusion based flow-matching (FM) model, with t…

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