{"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/cat-a-ctc-crf-based-asr-toolkit-bridging-the","title":"CAT: A CTC-CRF based ASR Toolkit Bridging the Hybrid and the End-to-end Approaches towards Data Efficiency and Low Latency","arxiv_id":"2005.13326","date":"2020-05-27","proceeding":null,"authors":["Keyu An","Hongyu Xiang","Zhijian Ou"],"abstract":"In this paper, we present a new open source toolkit for speech recognition, named CAT (CTC-CRF based ASR Toolkit). CAT inherits the data-efficiency of the hybrid approach and the simplicity of the E2E approach, providing a full-fledged implementation of CTC-CRFs and complete training and testing scripts for a number of English and Chinese benchmarks. Experiments show CAT obtains state-of-the-art results, which are comparable to the fine-tuned hybrid models in Kaldi but with a much simpler training pipeline. Compared to existing non-modularized E2E models, CAT performs better on limited-scale datasets, demonstrating its data efficiency. Furthermore, we propose a new method called contextualized soft forgetting, which enables CAT to do streaming ASR without accuracy degradation. We hope CAT, especially the CTC-CRF based framework and software, will be of broad interest to the community, and can be further explored and improved.","url_abs":"https://arxiv.org/abs/2005.13326v2","url_pdf":"https://arxiv.org/pdf/2005.13326v2.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":"cat-a-ctc-crf-based-asr-toolkit-bridging-the","repo_url":"https://github.com/thu-spmi/cat","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"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/speech-recognition-on-aishell-1","task":"Speech Recognition","dataset":"AISHELL-1","model":"CTC-CRF 4gram-LM","rank_in_archive_order":15,"of":18,"metrics":{"Word Error Rate (WER)":"6.34"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-hub5-00-fisher-swbd","task":"Speech Recognition","dataset":"Hub5'00 FISHER-SWBD","model":"CTC-CRF","rank_in_archive_order":1,"of":1,"metrics":{"Word Error Rate (WER)":"12"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-hub500-switchboard","task":"Speech Recognition","dataset":"Hub5'00 SwitchBoard","model":"CTC-CRF","rank_in_archive_order":4,"of":5,"metrics":{"CallHome":"18.4","Hub5'00":"14.1","SwitchBoard":"9.7"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-wsj-dev93","task":"Speech Recognition","dataset":"WSJ dev93","model":"CTC-CRF VGG-BLSTM","rank_in_archive_order":2,"of":4,"metrics":{"Word Error Rate (WER)":"5.7"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-wsj-eval92","task":"Speech Recognition","dataset":"WSJ eval92","model":"CTC-CRF VGG-BLSTM","rank_in_archive_order":9,"of":17,"metrics":{"Word Error Rate (WER)":"3.2"},"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}