{"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/fast-simpler-and-more-accurate-hybrid-asr","title":"Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces","arxiv_id":"2005.09150","date":"2020-05-19","proceeding":null,"authors":["Frank Zhang","Yongqiang Wang","Xiaohui Zhang","Chunxi Liu","Yatharth Saraf","Geoffrey Zweig"],"abstract":"In this work, we first show that on the widely used LibriSpeech benchmark, our transformer-based context-dependent connectionist temporal classification (CTC) system produces state-of-the-art results. We then show that using wordpieces as modeling units combined with CTC training, we can greatly simplify the engineering pipeline compared to conventional frame-based cross-entropy training by excluding all the GMM bootstrapping, decision tree building and force alignment steps, while still achieving very competitive word-error-rate. Additionally, using wordpieces as modeling units can significantly improve runtime efficiency since we can use larger stride without losing accuracy. We further confirm these findings on two internal VideoASR datasets: German, which is similar to English as a fusional language, and Turkish, which is an agglutinative language.","url_abs":"https://arxiv.org/abs/2005.09150v2","url_pdf":"https://arxiv.org/pdf/2005.09150v2.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":[],"tasks":[{"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 + Transformer LM rescoring","rank_in_archive_order":29,"of":64,"metrics":{"Word Error Rate (WER)":"2.10"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-librispeech-test-other","task":"Speech Recognition","dataset":"LibriSpeech test-other","model":"CTC + Transformer LM rescoring","rank_in_archive_order":20,"of":53,"metrics":{"Word Error Rate (WER)":"4.20"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2005.09150","atlas_url":"https://app.syntology.ai/?focus=2005.09150","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}