3 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Although reinforcement learning (RL) post-training repairs the localized segmental errors of zero-shot text-to-speech (TTS), arriving at a working recipe still relies on tedious manual tuning, and whether LLM agents can take over this research pipeline is unclear. We investigate this question with AgenticTTS-Forge, a c...
This work proposes to optimize the benchmark generation workflow itself end to end with MetaBench-Harness, a dual-loop search framework that enables multi-dimensional evolution, steadily improving evolution reasonableness, benchmark competency, and evaluator robustness across successive rounds.
Ruby-ASR is presented, which refines the conventional target into a span-bound orthographic--lexical-reading sequence and shows that refining the recognition target can improve lexical-reading recovery without sacrificing readable orthographic transcription.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
