7 citations · 10 across the 22 of their papers we have counts for
18 papers · 1 filter
Improving Text-to-Audio Instruction Following via Fine-Grained Feedback from Audio-Aware Large Language Models
Chun-Yi Kuan, Siwon Kim, Byeonggeun Kim +7
Recent text-to-audio models generate high-quality audio, but often fail to follow instructions involving multiple sound events and temporal order. This gap arises because existing…
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…
Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models
Chun-Yi Kuan, Wei-Ping Huang, Hung-yi Lee
Recent audio-aware large language models (ALLMs) have demonstrated strong capabilities across diverse audio understanding and reasoning tasks, but they still frequently produce hal…
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…
Game-Time: Evaluating Temporal Dynamics in Spoken Language Models
Kai-Wei Chang, En-Pei Hu, Chun-Yi Kuan +7
Conversational Spoken Language Models (SLMs) are emerging as a promising paradigm for real-time speech interaction. However, their capacity of temporal dynamics, including the abil…
DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +25
We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following. Recent LALMs augment Large Langu…
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…
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning
Prabhat Pandey, Rupak Vignesh Swaminathan, K V Vijay Girish +4
We introduce SIFT (Speech Instruction Fine-Tuning), a 50M-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). SIFT-50…
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…
Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language Models
Yi-Cheng Lin, Tzu-Quan Lin, Chih-Kai Yang +4
Speech Integrated Large Language Models (SILLMs) combine large language models with speech perception to perform diverse tasks, such as emotion recognition to speaker verification,…
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…
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…
Investigating Zero-Shot Generalizability on Mandarin-English Code-Switched ASR and Speech-to-text Translation of Recent Foundation Models with Self-Supervision and Weak Supervision
Chih-Kai Yang, Kuan-Po Huang, Ke-Han Lu +3
This work evaluated several cutting-edge large-scale foundation models based on self-supervision or weak supervision, including SeamlessM4T, SeamlessM4T v2, and Whisper-large-v3, o…
Towards General-Purpose Text-Instruction-Guided Voice Conversion
Chun-Yi Kuan, Chen An Li, Tsu-Yuan Hsu +5
This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice". Unlike tr…
Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech
Chien-yu Huang, Ke-Han Lu, Shih-Heng Wang +12
Text language models have shown remarkable zero-shot capability in generalizing to unseen tasks when provided with well-formulated instructions. However, existing studies in speech…