4 accepted papers
Tactile sensing is essential for robots to achieve human-like gentle manipulation capabilities. However, existing Vision-Language-Action (VLA) models struggle to exploit tactile feedback for gentle manipulation due to the scarcity of aligned vision-tactile-language data and the lack of effective clo…
Chatbots powered by large language models (LLMs) offer natural, human-like interactions. However, traditional text-to-speech (TTS) models paired with LLMs typically wait for the entire sentence to be generated before starting synthesis, leading to increased response latency. Although word-by-word sp…
Attention is the cornerstone of modern Large Language Models (LLMs). Yet its quadratic complexity hinders efficiency and scalability, especially for long-context processing. A promising approach is to leverage sparsity in attention. However, existing sparsity-based solutions predominantly rely on p…
Noisy Student Training (NST) has recently demonstrated extremely strong performance in Automatic Speech Recognition (ASR). In this paper, we propose a data selection strategy named LM Filter to improve the performance of NST on non-target domain data in ASR tasks. Hypotheses with and without a Langu…