Nowadays, non-privacy small-scale motion detection has attracted an increasing amount of research in remote sensing in speech recognition. These new modalities are employed to enhance and restore speech information from speakers of multiple types of data. In this paper, we propose a dataset contains 7.5 GHz Channel Impulse Response (CIR) data from ultra-wideband (UWB) radars, 77-GHz frequency modulated continuous wave (FMCW) data from millimetre wave (mmWave) radar, and laser data. Meanwhile, a depth camera is adopted to record the landmarks of the subject's lip and voice. Approximately 400 minutes of annotated speech profiles are provided, which are collected from 20 participants speaking 5 vowels, 15 words and 16 sentences. The dataset has been validated and has potential for the research of lip reading and multimodal speech recognition.
@article{arxiv.2303.08295,
title = {A large-scale multimodal dataset of human speech recognition},
author = {Yao Ge and Chong Tang and Haobo Li and Zikang Zhang and Wenda Li and Kevin Chetty and Daniele Faccio and Qammer H. Abbasi and Muhammad Imran},
journal= {arXiv preprint arXiv:2303.08295},
year = {2023}
}