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Yi-Cheng Lin

National Taiwan University

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Preprint Sep 2026

Rethinking Automated Voice Similarity by Shifting from EER to Embedding Geometry

Speaker verification (SV) models are commonly assumed to better capture nuances among speaker characteristics as verification accuracy improves, leading to their widespread use as automated proxies for human voice similarity in speech generation tasks. However, by establishing a human perceptual alignment metric and co...

Szu-Chi Chen, Jia-Tang Dong, Yi-Cheng Lin et al. · 0 citations

#artificial intelligence Preprint Oct 2026

When to Rethink: Learning Multi-Perspective Self-Verification for Vision-Language Models

Vision-language models (VLMs) have achieved strong performance in multimodal reasoning, yet they remain prone to generating plausible but incorrect answers. Self-verification offers a practical way to improve answer reliability without relying on external judges, but existing methods typically depend on a single verifi...

Zi-Quan Zhu, Han-Ruo Zhu, Si-Yuan Lu et al. · 0 citations

Preprint Sep 2026

Listen, Critique, and Refine: RL-Based Self-Refinement for Instruction-Following Speech Synthesis

This work training a LALM with reinforcement learning to reason over its own speech output generates a draft speech as a form of audio-token reasoning, critiques its own generation by reflecting on the acoustic realization in text, and produces a refined version conditioned on both the first-pass speech and the critiqu...

Chee-En Yu, Yi-Cheng Lin, Sung-Feng Huang et al. · 1 citation

Preprint Sep 2026

AudioICL-Bench: A Benchmark for Large Audio Language Model In-Context Learning

AudioICL-Bench is introduced, a diagnostic benchmark whose per-episode rules are resampled so that no correct answer is recoverable from prior knowledge, and its nine tasks are organized along two axes that separate what must be learned from demonstrations from what must be perceived in the signal, enabling failures to...

Jia-Hung Chen, Yi-Cheng Lin, Kai-Wei Chang et al. · 0 citations

Preprint Aug 2026

Findings of the First Teaching Monster Challenge: A Benchmark of Pedagogical Content Knowledge in AI Agents

The Teaching Monster Challenge is introduced, the first instructional video generation benchmark to treat the learner persona as an explicit evaluation criterion and release the benchmark, rubric, and human judgments as a testbed for both teaching systems and automatic judges.

Yi-Cheng Lin, Yu-Kai Guo, Szu-Chi Chen et al. · 0 citations