Dongha Ahn — accepted AI-conference papers

2026

NO VERIFIABLE REWARD FOR PROSODY: TOWARD PREFERENCE-GUIDED PROSODY LEARNING IN TTS

Seungyoun Shin, Dongha Ahn, Jiwoo Kim, Sungwook Jeon

ICASSP 2026poster

Recent work reports gains in neural text-to-speech (TTS) with Group Relative Policy Optimization (GRPO). However, in the absence of a verifiable reward for \textit{prosody}, GRPO trained on transcription-oriented signals (CER/NLL) lowers error rates yet collapses prosody into monotone, unnatural spe…

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

RoToR: Towards More Reliable Responses for Order-Invariant Inputs

Soyoung Yoon, Dongha Ahn, Youngwon Lee, Minkyu Jung, HyungJoo Jang, Seung-won Hwang

ACL 2025long

Mitigating positional bias of language models (LMs) for listwise inputs is a well-known and important problem (e.g., lost-in-the-middle). While zero-shot order-invariant LMs have been proposed to solve this issue, their success on practical listwise problems has been limited. In this work, as a firs…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗