3 citations · 5 across the 14 of their papers we have counts for
5 papers · 1 filter
Can Large Audio-Language Models Truly Hear? Tackling Hallucinations with Multi-Task Assessment and Stepwise Audio Reasoning
Chun-Yi Kuan, Hung-yi Lee
Recent advancements in large audio-language models (LALMs) have shown impressive capabilities in understanding and reasoning about audio and speech information. However, these mode…
Speech-Copilot: Leveraging Large Language Models for Speech Processing via Task Decomposition, Modularization, and Program Generation
Chun-Yi Kuan, Chih-Kai Yang, Wei-Ping Huang +2
In this work, we introduce Speech-Copilot, a modular framework for instruction-oriented speech-processing tasks that minimizes human effort in toolset construction. Unlike end-to-e…
Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course
Cheng-Han Chiang, Wei-Chih Chen, Chun-Yi Kuan +2
Using large language models (LLMs) for automatic evaluation has become an important evaluation method in NLP research. However, it is unclear whether these LLM-based evaluators can…
Understanding Sounds, Missing the Questions: The Challenge of Object Hallucination in Large Audio-Language Models
Chun-Yi Kuan, Wei-Ping Huang, Hung-yi Lee
Large audio-language models (LALMs) enhance traditional large language models by integrating audio perception capabilities, allowing them to tackle audio-related tasks. Previous re…
Maximizing Data Efficiency for Cross-Lingual TTS Adaptation by Self-Supervised Representation Mixing and Embedding Initialization
Wei-Ping Huang, Sung-Feng Huang, Hung-yi Lee
This paper presents an effective transfer learning framework for language adaptation in text-to-speech systems, with a focus on achieving language adaptation using minimal labeled…