{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/data-fusion-for-audiovisual-speaker","title":"Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain","arxiv_id":"2102.11588","date":"2021-02-23","proceeding":null,"authors":["Julio Wissing","Benedikt Boenninghoff","Dorothea Kolossa","Tsubasa Ochiai","Marc Delcroix","Keisuke Kinoshita","Tomohiro Nakatani","Shoko Araki","Christopher Schymura"],"abstract":"Estimating the positions of multiple speakers can be helpful for tasks like automatic speech recognition or speaker diarization. Both applications benefit from a known speaker position when, for instance, applying beamforming or assigning unique speaker identities. Recently, several approaches utilizing acoustic signals augmented with visual data have been proposed for this task. However, both the acoustic and the visual modality may be corrupted in specific spatial regions, for instance due to poor lighting conditions or to the presence of background noise. This paper proposes a novel audiovisual data fusion framework for speaker localization by assigning individual dynamic stream weights to specific regions in the localization space. This fusion is achieved via a neural network, which combines the predictions of individual audio and video trackers based on their time- and location-dependent reliability. A performance evaluation using audiovisual recordings yields promising results, with the proposed fusion approach outperforming all baseline models.","url_abs":"https://arxiv.org/abs/2102.11588v2","url_pdf":"https://arxiv.org/pdf/2102.11588v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"data-fusion-for-audiovisual-speaker","repo_url":"https://github.com/rub-ksv/spatial-stream-weights","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"automatic-speech-recognition-2","task_name":"Automatic Speech Recognition"},{"task_slug":"automatic-speech-recognition","task_name":"Automatic Speech Recognition (ASR)"},{"task_slug":null,"task_name":"Position"},{"task_slug":"speaker-diarization","task_name":"Speaker Diarization"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"speaker-diarization","task_name":"speaker-diarization"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2102.11588","atlas_url":"https://app.syntology.ai/?focus=2102.11588","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}