3 accepted papers
Large Audio Language Models (LALMs) have emerged as powerful tools for speech-related tasks but remain underexplored for fine-tuning, especially with limited speech data. To bridge this gap, we systematically examine how different fine-tuning schemes including text-only, direct mixing, and curriculu…
The streaming Sound EnhAncement Network (SEANet) has demonstrated impressive performance for speech bandwidth extension (BWE) with low latency and computational complexity. Although the streaming SEANet was designed for voice communication systems, it was not tested with decoded signals that include…
Many of real-world data, e.g., the VGGFace2 dataset, which is a collection of multiple portraits of individuals, come with nested structures due to grouped observation. The Ornstein auto-encoder (OAE) is an emerging framework for representation learning from nested data, based on an optimal transpor…