{"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/mitigating-the-impact-of-speech-recognition-1","title":"Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation","arxiv_id":"1904.07904","date":"2019-04-16","proceeding":null,"authors":["Chia-Hsuan Lee","Yun-Nung Chen","Hung-Yi Lee"],"abstract":"Spoken question answering (SQA) is challenging due to complex reasoning on\ntop of the spoken documents. The recent studies have also shown the\ncatastrophic impact of automatic speech recognition (ASR) errors on SQA.\nTherefore, this work proposes to mitigate the ASR errors by aligning the\nmismatch between ASR hypotheses and their corresponding reference\ntranscriptions. An adversarial model is applied to this domain adaptation task,\nwhich forces the model to learn domain-invariant features the QA model can\neffectively utilize in order to improve the SQA results. The experiments\nsuccessfully demonstrate the effectiveness of our proposed model, and the\nresults are better than the previous best model by 2% EM score.","url_abs":"http://arxiv.org/abs/1904.07904v1","url_pdf":"http://arxiv.org/pdf/1904.07904v1.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":"mitigating-the-impact-of-speech-recognition-1","repo_url":"https://github.com/chia-hsuan-lee/spoken-squad","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"mitigating-the-impact-of-speech-recognition-1","repo_url":"https://github.com/chiahsuan156/Spoken-SQuAD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"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":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"spoken-language-understanding","task_name":"Spoken Language Understanding"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/spoken-language-understanding-on-spoken-squad","task":"Spoken Language Understanding","dataset":"Spoken-SQuAD","model":"QANet + GAN","rank_in_archive_order":3,"of":4,"metrics":{"F1 score":"63.11"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}