3 citations · 5 across the 14 of their papers we have counts for
9 papers · 1 filter
The Sound of Absence: Audio-Language Embedding Models Struggle with Negation
Chun-Yi Kuan, Hung-yi Lee
Audio-language embedding models such as CLAP are widely evaluated on matching present sound events, but rarely on negation. We show this affirmation-only evaluation hides a key lim…
AQAScore: Evaluating Semantic Alignment in Text-to-Audio Generation via Audio Question Answering
Chun-Yi Kuan, Kai-Wei Chang, Hung-yi Lee
Although text-to-audio generation has made remarkable progress in realism and diversity, the development of evaluation metrics has not kept pace. Widely-adopted approaches, typical…
AQUA-Bench: Beyond Finding Answers to Knowing When There Are None in Audio Question Answering
Chun-Yi Kuan, Hung-yi Lee
Recent advances in audio-aware large language models have shown strong performance on audio question answering. However, existing benchmarks mainly cover answerable questions and o…
Teaching Audio-Aware Large Language Models What Does Not Hear: Mitigating Hallucinations through Synthesized Negative Samples
Chun-Yi Kuan, Hung-yi Lee
Recent advancements in audio-aware large language models (ALLMs) enable them to process and understand audio inputs. However, these models often hallucinate non-existent sound even…
Speech-IFEval: Evaluating Instruction-Following and Quantifying Catastrophic Forgetting in Speech-Aware Language Models
Ke-Han Lu, Chun-Yi Kuan, Hung-yi Lee
We introduce Speech-IFeval, an evaluation framework designed to assess instruction-following capabilities and quantify catastrophic forgetting in speech-aware language models (SLMs…
From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data
Chun-Yi Kuan, Hung-yi Lee
Audio-aware large language models (ALLMs) have recently made great strides in understanding and processing audio inputs. These models are typically adapted from text-based large la…