{"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/crf-based-single-stage-acoustic-modeling-with","title":"CRF-based Single-stage Acoustic Modeling with CTC Topology","arxiv_id":null,"date":"2019-04-16","proceeding":null,"authors":["Hongyu Xiang","Zhijian Ou"],"abstract":"In this paper, we develop conditional random field (CRF) based single-stage (SS) acoustic modeling with connectionist temporal classification (CTC) inspired state topology, which is called CTC-CRF for short.\r\nCTC-CRF is conceptually simple, which basically implements a CRF layer on top of features generated by the bottom neural network with the special state topology.\r\nLike SS-LF-MMI (lattice-free maximum-mutual-information), CTC-CRFs can be trained from scratch (flat-start), eliminating GMM-HMM pre-training and tree-building.\r\nEvaluation experiments are conducted on the WSJ, Switchboard and Librispeech datasets.\r\nIn a head-to-head comparison, the CTC-CRF model using simple Bidirectional LSTMs consistently outperforms the strong SS-LF-MMI, across all the three benchmarking datasets and in both cases of mono-phones and mono-chars.\r\nAdditionally, CTC-CRFs avoid some ad-hoc operation in SS-LF-MMI.","url_abs":"https://ieeexplore.ieee.org/document/8682256","url_pdf":"http://oa.ee.tsinghua.edu.cn/~ouzhijian/pdf/ctc-crf.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":"crf-based-single-stage-acoustic-modeling-with","repo_url":"https://github.com/thu-spmi/cat","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-recognition-on-librispeech-test-clean","task":"Speech Recognition","dataset":"LibriSpeech test-clean","model":"CTC-CRF 4gram-LM","rank_in_archive_order":52,"of":64,"metrics":{"Word Error Rate (WER)":"4.09"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-librispeech-test-other","task":"Speech Recognition","dataset":"LibriSpeech test-other","model":"CTC-CRF 4gram-LM","rank_in_archive_order":48,"of":53,"metrics":{"Word Error Rate (WER)":"10.65"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-wsj-dev93","task":"Speech Recognition","dataset":"WSJ dev93","model":"Convolutional Speech Recognition","rank_in_archive_order":3,"of":4,"metrics":{"Word Error Rate (WER)":"6.23"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-wsj-eval92","task":"Speech Recognition","dataset":"WSJ eval92","model":"CTC-CRF 4gram-LM","rank_in_archive_order":15,"of":17,"metrics":{"Word Error Rate (WER)":"3.79"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-wsj-eval93","task":"Speech Recognition","dataset":"WSJ eval93","model":"CTC-CRF 4gram-LM","rank_in_archive_order":2,"of":3,"metrics":{"Word Error Rate (WER)":"6.23"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}