3 accepted papers
The goal of this paper is to provide a new perspective on speech modeling by incorporating perceptual invariances such as amplitude scaling and temporal shifts. Conventional generative formulations often treat each dataset sample as a fixed representative of the target distribution. From a generativ…
The goal of this work is to simultaneously generate natural talking faces and speech outputs from text. We achieve this by integrating Talking Face Generation (TFG) and Text-to-Speech (TTS) systems into a unified framework. We address the main challenges of each task: (1) generating a range of head…
This paper presents a multi-modal dataset that contains rich transcriptions of spoken conversations. As diverse multi-modal and multi-task models emerge, there is a growing need for multi-modal training and evaluation datasets accompanied by rich metadata. However, there is no universal dataset that…