Papers by Yi-Cheng Wang
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models (2024.findings-acl)
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Haibin Wu, Ho-Lam Chung, Yi-Cheng Lin, Yuan-Kuei Wu, Xuanjun Chen, Yu-Chi Pai, Hsiu-Hsuan Wang, Kai-Wei Chang, Alexander Liu, Hung-yi Lee
| Challenge: | Researchers have developed a sound codec that can be used as tokenizers for preserving audio data and minimizing data transmission latency. |
| Approach: | They propose to use codec-SUPERB to assess codec models across representative sound applications and signal-level metrics rooted in sound domain knowledge. |
| Outcome: | The proposed codec-SUPERB model is evaluated on selected experimental settings. |
An Effective Pronunciation Assessment Approach Leveraging Hierarchical Transformers and Pre-training Strategies (2024.acl-long)
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Bi-Cheng Yan, Jiun-Ting Li, Yi-Cheng Wang, Hsin Wei Wang, Tien-Hong Lo, Yung-Chang Hsu, Wei-Cheng Chao, Berlin Chen
| Challenge: | Existing attempts to quantify a second language learner’s pronunciation proficiency in a target language often sideline the hierarchy of linguistic units and relatedness among the pronunciation aspects. |
| Approach: | They propose a hierarchical automatic pronunciation assessment method that models the intrinsic structures of an utterance while considering the relatedness among the pronunciation aspects. |
| Outcome: | The proposed method can be used to quantify a second language learner’s pronunciation proficiency in a target language by providing fine-grained feedback with multiple pronunciation aspect scores at various linguistic levels. |
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition (2024.lrec-main)
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| Challenge: | End-to-end automatic speech recognition systems suffer from mistranscription of domain-specific phrases, such as named entities. |
| Approach: | They propose a named entity correction model that leverages phonetic con-fusion to mitigate phonetic confusion. |
| Outcome: | The proposed model outperforms the existing model on AISHELL-1 and Homophone datasets. |
MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation (2026.acl-long)
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| Challenge: | Existing RAG solutions for large language models are limited by context windows limiting their ability to process long-form, domain-specific content. |
| Approach: | They propose a multimodal knowledge graph-based RAG that enables cross-modal reasoning . their method incorporates visual cues into the construction of knowledge graphs, retrieval phase, and answer generation process . |
| Outcome: | Experimental results show that the proposed approach outperforms existing approaches on textual and multimodal benchmarks. |