Papers by Chao-Han Huck Yang
MISP-Meeting: A Real-World Dataset with Multimodal Cues for Long-form Meeting Transcription and Summarization (2025.acl-long)
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| Challenge: | Existing systems that can recognize spoken content, extract key information, and produce concise summaries are lacking in meeting transcription and summarization. |
| Approach: | They propose a multimodal dataset that integrates information from speech, vision, and text modalities to facilitate automatic meeting transcription and summarization (AMTS). |
| Outcome: | The proposed dataset reduces the character error rate (CER) by 36.60% to 20.27% and improves speech recognition and large language models. |
WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations (2026.findings-acl)
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| Challenge: | Large audio-language models extend language understanding into the auditory domain, yet their ability to perform low-level listening, such as pitch and duration detection, remains underexplored. |
| Approach: | They propose a global benchmark to evaluate low-level auditory perception and cognition using marine mammal vocalizations to better assess models’ low- level listening. |
| Outcome: | The proposed models show performance far below human levels, indicating a need for stronger auditory grounding in LALMs. |
Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception (2026.acl-long)
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Zhen Wan, Chao-Han Huck Yang, Jinchuan Tian, Hanrong Ye, Ankita Pasad, Szu-Wei Fu, Arushi Goel, Ryo Hachiuma, Shizhe Diao, Kunal Dhawan, Sreyan Ghosh, Yusuke Hirota, Zhehuai Chen, Rafael Valle, Chenhui Chu, Shinji Watanabe, Boris Ginsburg, Yu-Chiang Frank Wang
| Challenge: | naively fine-tuning an omni-model on speech recognition and external sound understanding tasks often degrades performance . Xie and Wu's framework, Speech-Hands, recasts the problem as an explicit self-reflection decision. |
| Approach: | They propose a voice-agentic framework that learns one critical omni-understanding skill: trusting itself versus external audio perception. |
| Outcome: | The proposed framework outperforms baseline models on the OpenASR leaderboard by 12.1% WER and high F1 on audio QA decisions. |
SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)
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Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi
| Challenge: | SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Approach: | They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Outcome: | The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice. |
PRiSM: Benchmarking Phone Realization in Speech Models (2026.acl-long)
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Shikhar Bharadwaj, Chin-Jou Li, Yoonjae Kim, Kwanghee Choi, Eunjung Yeo, Ryan Soh-Eun Shim, Hanyu Zhou, Brendon Boldt, Karen Rosero, Kalvin Chang, Darsh Agrawal, Keer Xu, Chao-Han Huck Yang, Jian Zhu, Shinji Watanabe, David R. Mortensen
| Challenge: | Existing evaluations of phone recognition systems only measure surface-level transcription accuracy. |
| Approach: | They propose to standardize transcription-based evaluation and assess downstream utility in clinical, educational, and multilingual settings with transcription and representation probes. |
| Outcome: | The proposed system outperforms LALMs in clinical, educational, and multilingual settings. |
CoVoGER: A Multilingual Multitask Benchmark for Speech-to-text Generative Error Correction with Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models can fix recognition or translation errors that traditional rescoring cannot fix. |
| Approach: | They propose a benchmark for GER that covers both ASR and speech-to-text translation across 15 languages and 28 language pairs. |
| Outcome: | The proposed benchmark is built on common voice 20.0 and CoVoST-2 with Whisper and SeamlessM4T. |
LOTUS: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences (2025.acl-industry)
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Yusuke Hirota, Boyi Li, Ryo Hachiuma, Yueh-Hua Wu, Boris Ivanovic, Marco Pavone, Yejin Choi, Yu-Chiang Frank Wang, Yuta Nakashima, Chao-Han Huck Yang
| Challenge: | Large Vision-Language Models (LVLMs) have transformed image captioning . existing evaluations lack standardized criteria and a standardized evaluation framework . |
| Approach: | They propose a leaderboard for evaluating detailed captions that addresses three main gaps in existing evaluations: lack of standardized criteria, bias-aware assessments, and user preference considerations. |
| Outcome: | The proposed model evaluates caption quality, descriptiveness, risks, and societal biases while tailoring criteria to user preferences. |
Extending Automatic Machine Translation Evaluation to Book-Length Documents (2025.emnlp-main)
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Kuang-Da Wang, Shuoyang Ding, Chao-Han Huck Yang, Ping-Chun Hsieh, Wen-Chih Peng, Vitaly Lavrukhin, Boris Ginsburg
| Challenge: | Large Language Models (LLMs) have superior translation performance and long-context capabilities, but evaluation methodologies remain constrained to sentence-level assessment due to dataset limitations and token number restrictions in metrics. |
| Approach: | They propose an evaluation scheme that extends existing automatic metrics to long-document translation by treating documents as continuous text and applying sentence segmentation and alignment methods. |
| Outcome: | The proposed evaluation scheme outperforms existing long-form document evaluation schemes while accounting for under-/over-translations and varied sentence boundaries. |
NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model (2025.acl-industry)
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Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko, Zhen Wan, Xuesong Yang, Zih-Ching Chen, Krishna C Puvvada, Ke Hu, Szu-Wei Fu, Jun Wei Chiu, Jagadeesh Balam, Boris Ginsburg, Yu-Chiang Frank Wang, Chao-Han Huck Yang
| Challenge: | Existing methods to train a model on a mixture of domain datasets require separate correction language models. |
| Approach: | They propose a multi-task correction MoE that trains experts to become an "expert" of speech-to-text, language-totext and vision-to text datasets by learning to route each dataset’s tokens to its mapped expert. |
| Outcome: | The proposed model outperforms GPT-3.5 and Claude-3.5-Sonnet on the Open ASR Leaderboard and reaches an average relative 5.0% WER reduction and substantial improvements in BLEU scores. |