{"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/end-to-end-audiovisual-speech-recognition","title":"End-to-end Audiovisual Speech Recognition","arxiv_id":"1802.06424","date":"2018-02-18","proceeding":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018 9","authors":["Stavros Petridis","Themos Stafylakis","Pingchuan Ma","Feipeng Cai","Georgios Tzimiropoulos","Maja Pantic"],"abstract":"Several end-to-end deep learning approaches have been recently presented\nwhich extract either audio or visual features from the input images or audio\nsignals and perform speech recognition. However, research on end-to-end\naudiovisual models is very limited. In this work, we present an end-to-end\naudiovisual model based on residual networks and Bidirectional Gated Recurrent\nUnits (BGRUs). To the best of our knowledge, this is the first audiovisual\nfusion model which simultaneously learns to extract features directly from the\nimage pixels and audio waveforms and performs within-context word recognition\non a large publicly available dataset (LRW). The model consists of two streams,\none for each modality, which extract features directly from mouth regions and\nraw waveforms. The temporal dynamics in each stream/modality are modeled by a\n2-layer BGRU and the fusion of multiple streams/modalities takes place via\nanother 2-layer BGRU. A slight improvement in the classification rate over an\nend-to-end audio-only and MFCC-based model is reported in clean audio\nconditions and low levels of noise. In presence of high levels of noise, the\nend-to-end audiovisual model significantly outperforms both audio-only models.","url_abs":"http://arxiv.org/abs/1802.06424v2","url_pdf":"http://arxiv.org/pdf/1802.06424v2.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":"end-to-end-audiovisual-speech-recognition","repo_url":"https://github.com/mpc001/end-to-end-Lipreading","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"end-to-end-audiovisual-speech-recognition","repo_url":"https://github.com/tstafylakis/Lipreading-ResNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"lipreading","task_name":"Lipreading"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lipreading-on-lip-reading-in-the-wild","task":"Lipreading","dataset":"Lip Reading in the Wild","model":"3D Conv + ResNet-34 + Bi-GRU","rank_in_archive_order":20,"of":22,"metrics":{"Top-1 Accuracy":"83.39"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1802.06424","atlas_url":"https://app.syntology.ai/?focus=1802.06424","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}