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Readme_2109.docx

readme 7924 1 48252 document 7924 indexcodes.txt text/plain a05ea1e8f0c2b59e47f647bcfae10da4 MD5 3555 2026-06-29 08:18:42 https://researchdata.gla.ac.uk/2109/2/indexcodes.txt 2109 2 2 text/plain other Generate index codes conversion from Text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://researchdata.gla.ac.uk/id/document/7923 http://eprints.org/relation/isVolatileVersionOf https://researchdata.gla.ac.uk/id/document/7923 http://eprints.org/relation/isIndexCodesVersionOf https://researchdata.gla.ac.uk/id/document/7923 archive 32966 disk0/00/00/21/09 2026-06-29 08:28:20 2026-06-29 10:36:03 2026-06-29 08:28:20 data_collection show Ghadban Nour 68735 Hameed Hira 64581 Abbasi Qammer H. 39488 0000-0002-7097-9969 Imran Muhammad 37001 0000-0003-4743-9136 Cooper Jon 7063 0000-0002-2358-1050 Le Kernec Julien 35927 0000-0003-2124-6803 glaresearchdata:2025-12-05-2109 pub 30300000 30303000 30305000 30304000 The data file associated with this dataset is too large for standard download (~17GB). Please use the request data button to arrange alternative access. This dataset is licensed with a Creative Commons Attribution CC-BY 4.0 license. The study uses a multimodal dataset comprising synchronized audio, video, and radar recordings collected to evaluate the effectiveness of audio–visual–radar (AVR) fusion in speech-recognition tasks. The dataset contains 800 labeled samples, produced by four fluent but non-native English speakers aged 28–40, each repeating ten phonetically challenging English words twenty times under controlled recording conditions. Audio was recorded at 44.1 kHz/16-bit, video at 1080p/30 fps, and radar data were acquired using the XeThru X4M03 IR-UWB sensor positioned approximately 2 meters from the speaker to capture speech-related micro-Doppler articulatory signatures. The three modalities were automatically segmented and aligned using pretrained machine-learning models to ensure consistent synchronization across streams. The purpose of this dataset is to serve as a resource for studying how radar sensing can enhance speech-recognition robustness under noise, occlusion, and other adverse conditions. All audio–visual recordings contain identifiable human data and will therefore be shared only under controlled access to protect participant privacy. 2025-12-05 published University of Glasgow 10.5525/gla.researchdata.2109 Audio Text Video University of Glasgow 307829 Quantum-Inspired Imaging for Remote Monitoring of Health & Disease in Community Healthcare Jonathan Cooper Engineering and Physical Sciences Research Council (EPSRC) EP/T021020/1 ENG - Biomedical Engineering FALSE English 2022-11-29 2022-12-19 https://doi.org/10.5525/gla.researchdata.2111 code 2035-12-05 R file_share TRUE 300230076 cose TRUE https://eprints.gla.ac.uk/id/eprint/352488 https://eprints.gla.ac.uk/id/eprint/348868 https://eprints.gla.ac.uk/id/eprint/345447 https://eprints.gla.ac.uk/id/eprint/341571 https://eprints.gla.ac.uk/id/eprint/386960