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DLLM-TTS framework enables faster, more accurate text-to-speech synthesis

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced DLLM-TTS, a novel text-to-speech synthesis framework that addresses the trade-off between speech intelligibility and generation speed. This new approach formulates TTS as conditional block discrete diffusion over X-Codec2 neural audio codec tokens. By decomposing sequences into blocks and applying masked diffusion within each block, DLLM-TTS achieves efficient, parallel generation with a real-time factor of 0.15, while maintaining competitive performance on benchmarks like Seed-TTS-eval. AI

IMPACT This framework offers a potential path to more efficient and data-efficient speech synthesis, improving real-time generation capabilities.

RANK_REASON The cluster describes a new research paper detailing a novel framework for text-to-speech synthesis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

DLLM-TTS framework enables faster, more accurate text-to-speech synthesis

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The cluster describes a new research paper detailing a novel framework for text-to-speech synthesis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Wasim Madha, Nityanand Mathur, Hamees Sayed, Apoorv Singh, Sameer Khurana, Akshat Mandloi, Sudarshan Kamath ·

    DLLM-TTS: Block Discrete Diffusion Language Model for Text-to-Speech Synthesis

    arXiv:2608.00011v1 Announce Type: new Abstract: Current text-to-speech systems face a trade-off: autoregres- sive codec language models produce highly intelligible speech but require large-scale models and training data and decode tokens sequentially, while non-autoregressive app…

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