From the 2 of 21 linked papers with an AI index.
21 papers
REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
Cheng-Kang Chou, Ming-To Chuang, Ke-Han Lu +2
The paper investigates drift in model-generated timestamps for autoregressive ASR systems and introduces REDDIT, a replay‑based distribution editing post‑training method that corre…
Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models
Yu-Han Huang, Chih-Kai Yang, Ke-Han Lu +2
The paper proposes IAAN, a training‑free method that identifies and amplifies specific neurons inside the audio encoder of large audio‑language models to improve recognition of fin…
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…
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…