4 accepted papers
Large Audio Language Models (LALMs) represent an important frontier in multimodal AI, addressing diverse audio tasks. Recently, post-training of LALMs has received increasing attention due to significant performance improvements over foundation models. While single-stage post-training such as reinfo…
Deep learning has achieved success across many domains when sufficient training samples are available. However, the commonly used mini-batch stochastic gradient descent (SGD) training paradigm treats each sample equally, resulting in massive computational waste on samples that are easily identifiabl…
Personalized keyword spotting (KWS) with few enrollment utterances remains an important problem over years. KWS remains a challenging task due to the following factors, including the scarcity of enrollment samples, speech variation in the open-set scenarios, and distributional gap between source and…
Keyword Spotting (KWS) is crucial for hands-free voice-activated systems, requiring a balance between accuracy and complexity, especially in noisy environments. While Speech Enhancement (SE) can improve KWS accuracy, existing methods often lack the ability to effectively utilize the rich features pr…