{"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/a-survey-of-recent-dnn-architectures-on-the","title":"A Survey of Recent DNN Architectures on the TIMIT Phone Recognition Task","arxiv_id":"1806.07974","date":"2018-06-19","proceeding":null,"authors":["Josef Michalek","Jan Vanek"],"abstract":"In this survey paper, we have evaluated several recent deep neural network\n(DNN) architectures on a TIMIT phone recognition task. We chose the TIMIT\ncorpus due to its popularity and broad availability in the community. It also\nsimulates a low-resource scenario that is helpful in minor languages. Also, we\nprefer the phone recognition task because it is much more sensitive to an\nacoustic model quality than a large vocabulary continuous speech recognition\n(LVCSR) task. In recent years, many DNN published papers reported results on\nTIMIT. However, the reported phone error rates (PERs) were often much higher\nthan a PER of a simple feed-forward (FF) DNN. That was the main motivation of\nthis paper: To provide a baseline DNNs with open-source scripts to easily\nreplicate the baseline results for future papers with lowest possible PERs.\nAccording to our knowledge, the best-achieved PER of this survey is better than\nthe best-published PER to date.","url_abs":"http://arxiv.org/abs/1806.07974v1","url_pdf":"http://arxiv.org/pdf/1806.07974v1.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":"a-survey-of-recent-dnn-architectures-on-the","repo_url":"https://github.com/OrcusCZ/NNAcousticModeling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"survey","task_name":"Survey"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}