{"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/jointly-learning-to-align-and-convert","title":"Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models","arxiv_id":"1610.06540","date":"2016-10-20","proceeding":null,"authors":["Shubham Toshniwal","Karen Livescu"],"abstract":"We propose an attention-enabled encoder-decoder model for the problem of\ngrapheme-to-phoneme conversion. Most previous work has tackled the problem via\njoint sequence models that require explicit alignments for training. In\ncontrast, the attention-enabled encoder-decoder model allows for jointly\nlearning to align and convert characters to phonemes. We explore different\ntypes of attention models, including global and local attention, and our best\nmodels achieve state-of-the-art results on three standard data sets (CMUDict,\nPronlex, and NetTalk).","url_abs":"http://arxiv.org/abs/1610.06540v1","url_pdf":"http://arxiv.org/pdf/1610.06540v1.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":"jointly-learning-to-align-and-convert","repo_url":"https://github.com/shtoshni92/g2p","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":null,"task_name":"Grapheme-to-Phoneme Conversion"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}