arXiv (All)
open access
RAWD-TTS: Ratio-Free Reward Alignment for Discrete-Diffusion Voice Cloning
Zero-shot text-to-speech synthesizes new utterances in a speaker's voice from a short reference recording. Voice cloning requires accurate content and preserved speaker identity, but supervised acoustic-token prediction does not directly optimize these waveform-level properties. Reward-based post-tr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Taming Long-form Text-to-Speech#1123396
- Voices as Handles: Reasoning about Speaker Identity with Frozen Text LLMs#1143102
- SceneTTS-Bench: A Benchmark for Scene-Level TTS in Drama Dubbing#1062155
- CTC-TTS: LLM-based dual-streaming text-to-speech with CTC alignment#984119
- Lexical Decision with Real and Deepfake-Generated Voice#331629
Record · ID 1132229
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive