3 papers
Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs
Ganesh Pavan Kartikeya Bharadwaj Kolluri, Michael Kampouridis, Ravi Shekhar
Speech-LLMs are expensive to run, making compression important for real-world deployment. However, compressed models are usually selected using aggregate word error rate (WER), whi…
CARD: Cross-component Audio Representation Distillation for Encoder-Free Audio Captioning
Ganesh Pavan Kartikeya Bharadwaj Kolluri, Yuchen Zhang, Michael Kampouridis +1
Modern automated audio captioning systems pair a frozen audio encoder with a large language model (LLM) via a trainable projector, incurring the encoder's inference cost and bottle…
On the Role of Encoder Depth: Pruning Whisper and LoRA Fine-Tuning in SLAM-ASR
Ganesh Pavan Kartikeya Bharadwaj Kolluri, Michael Kampouridis, Ravi Shekhar
Automatic speech recognition (ASR) has advanced rapidly in recent years, driven by large-scale pretrained models and end-to-end architectures such as SLAM-ASR. A key component of S…