Papers by Ho-Lam Chung
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models (2024.findings-acl)
Copied to clipboard
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. |
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies. (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing distractor generation methods are far from practical, and there are still room for improvement. |
| Approach: | They propose a distractor generation scheme with multi-tasking and negative answer training strategies for generating multiple distractors. |
| Outcome: | The proposed scheme improves the state-of-the-art results from 28.65 to 39.81 (BLEU 1 score) and generates multiple distractors shows strong distracting power for multiple choice questions. |
LLM-Codec: Neural Audio Codec Meets Language Model Objectives (2026.findings-acl)
Copied to clipboard
| Challenge: | Neural audio codecs are optimized for waveform reconstruction rather than autoregressive prediction. |
| Approach: | They propose to augment codec training with language-model-facing objectives while keeping both codec and LLM architectures unchanged. |
| Outcome: | The proposed model improves speech coherence and predictability by preserving the semantic alignment between audio and text representations. |