15 citations · 23 across the 29 of their papers we have counts for
11 papers · 1 filter
AudioICL-Bench: A Benchmark for Large Audio Language Model In-Context Learning
Jia-Hung Chen, Yi-Cheng Lin, Kai-Wei Chang +2
In-context learning (ICL) promises training-free adaptation for audio, where labeling every new condition is costly. Yet existing audio ICL studies largely measure Task Recognition…
REDDIT: Forgetting-Resistant Correction of Timestamp Drift in ASR via Replay-Based Distribution Editing
Cheng-Kang Chou, Ming-To Chuang, Ke-Han Lu +2
Modern autoregressive ASR systems can emit timestamps as decoded tokens, enabling timestamped transcription without frame-level aligners or inference-time post-processing. We show…
Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models
Yu-Han Huang, Chih-Kai Yang, Ke-Han Lu +2
Large audio-language models (LALMs) often underperform on fine-grained, non-semantic attributes of speech, such as a speaker's emotion, despite strong performance on speech content…
Compress the Cache, Not the Speech Embedding: KV Compression for Efficient Speech LLMs
Ke-Han Lu, Keqi Deng, Ruchao Fan +2
Speech large language models (Speech LLMs) typically encode speech into sequences far longer than text, creating a major efficiency bottleneck during autoregressive decoding. A com…
Escaping the Procrustean Bed: Groupwise Orthogonal Connectors for Audio-Language Models
Ho-Lam Chung, Ke-Han Lu, Yi-Cheng Lin +3
Audio-language models compress a speech encoder's output through a Querying Transformer (Q-Former) connector before feeding it to a large language model. We identify two failures i…
CAAD: Contrastive Audio-Aware Distillation for Efficient Speech Language Models
Chun-Wei Chen, Tzu-Quan Lin, Ke-Han Lu +2
Speech Language Models achieve reasoning capabilities, but are often hindered by massive parameter counts and a tendency to prioritize linguistic priors over acoustic features. Whi…
All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation
Leonardo Haw-Yang Foo, Chih-Kai Yang, Chen-An Li +2
Large Audio-Language Models show consistent performance gains across speech and audio benchmarks, yet high scores may not reflect true auditory perception. If a model can answer qu…
How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation
Ke-Han Lu, Szu-Wei Fu, Chao-Han Huck Yang +13
Large language models (LLMs) have been widely used as knowledge backbones of Large Audio Language Models (LALMs), yet how much auditory knowledge they encode through text-only pre-…
MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models
Chih-Kai Yang, Yun-Shao Tsai, Yu-Kai Guo +7
While multi-audio understanding is critical for large audio-language models (LALMs), it remains underexplored. We introduce MUGEN, a comprehensive benchmark evaluating this capabil…
TW-Sound580K: A Regional Audio-Text Dataset with Verification-Guided Curation for Localized Audio-Language Modeling
Hao-Hui Xie, Ho-Lam Chung, Yi-Cheng Lin +4
Large Audio-Language Models (LALMs) typically struggle with localized dialectal prosody due to the scarcity of specialized corpora. We present TW-Sound580K, a Taiwanese audio-text…
MusicSem: A Semantically Rich Language--Audio Dataset of Natural Music Descriptions
Rebecca Salganik, Teng Tu, Fei-Yueh Chen +8
Music representation learning is central to music information retrieval and generation. While recent advances in multimodal learning have improved alignment between text and audio…