{"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/importantaug-a-data-augmentation-agent-for","title":"ImportantAug: a data augmentation agent for speech","arxiv_id":"2112.07156","date":"2021-12-14","proceeding":"ICASSP 2022 4","authors":["Viet Anh Trinh","Hassan Salami Kavaki","Michael I Mandel"],"abstract":"We introduce ImportantAug, a technique to augment training data for speech classification and recognition models by adding noise to unimportant regions of the speech and not to important regions. Importance is predicted for each utterance by a data augmentation agent that is trained to maximize the amount of noise it adds while minimizing its impact on recognition performance. The effectiveness of our method is illustrated on version two of the Google Speech Commands (GSC) dataset. On the standard GSC test set, it achieves a 23.3% relative error rate reduction compared to conventional noise augmentation which applies noise to speech without regard to where it might be most effective. It also provides a 25.4% error rate reduction compared to a baseline without data augmentation. Additionally, the proposed ImportantAug outperforms the conventional noise augmentation and the baseline on two test sets with additional noise added.","url_abs":"https://arxiv.org/abs/2112.07156v2","url_pdf":"https://arxiv.org/pdf/2112.07156v2.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":"importantaug-a-data-augmentation-agent-for","repo_url":"https://github.com/tvanh512/importantAug","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"keyword-spotting","task_name":"Keyword Spotting"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"}],"methods":[],"datasets_introduced":[{"slug":"google-speech-commands-musan","name":"Google Speech Commands - Musan","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/keyword-spotting-on-google-speech-commands","task":"Keyword Spotting","dataset":"Google Speech Commands","model":"ImportantAug","rank_in_archive_order":35,"of":42,"metrics":{"Google Speech Command-Musan":"86.7","Google Speech Commands V2 35":"95"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-google-speech-commands","task":"Speech Recognition","dataset":"Google Speech Commands - Musan","model":"ImportantAug","rank_in_archive_order":1,"of":1,"metrics":{"Error rate - SNR 0dB":"13.3"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}