{"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/hierarchical-pronunciation-assessment-with","title":"Hierarchical Pronunciation Assessment with Multi-Aspect Attention","arxiv_id":"2211.08102","date":"2022-11-15","proceeding":null,"authors":["Heejin Do","Yunsu Kim","Gary Geunbae Lee"],"abstract":"Automatic pronunciation assessment is a major component of a computer-assisted pronunciation training system. To provide in-depth feedback, scoring pronunciation at various levels of granularity such as phoneme, word, and utterance, with diverse aspects such as accuracy, fluency, and completeness, is essential. However, existing multi-aspect multi-granularity methods simultaneously predict all aspects at all granularity levels; therefore, they have difficulty in capturing the linguistic hierarchy of phoneme, word, and utterance. This limitation further leads to neglecting intimate cross-aspect relations at the same linguistic unit. In this paper, we propose a Hierarchical Pronunciation Assessment with Multi-aspect Attention (HiPAMA) model, which hierarchically represents the granularity levels to directly capture their linguistic structures and introduces multi-aspect attention that reflects associations across aspects at the same level to create more connotative representations. By obtaining relational information from both the granularity- and aspect-side, HiPAMA can take full advantage of multi-task learning. Remarkable improvements in the experimental results on the speachocean762 datasets demonstrate the robustness of HiPAMA, particularly in the difficult-to-assess aspects.","url_abs":"https://arxiv.org/abs/2211.08102v2","url_pdf":"https://arxiv.org/pdf/2211.08102v2.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":"hierarchical-pronunciation-assessment-with","repo_url":"https://github.com/doheejin/HiPAMA","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"phone-level-pronunciation-scoring","task_name":"Phone-level pronunciation scoring"},{"task_slug":"utterance-level-pronounciation-scoring","task_name":"Utterance-level pronounciation scoring"},{"task_slug":"word-level-pronunciation-scoring","task_name":"Word-level pronunciation scoring"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/phone-level-pronunciation-scoring-on","task":"Phone-level pronunciation scoring","dataset":"speechocean762","model":"HiPAMA-Librispeech","rank_in_archive_order":6,"of":8,"metrics":{"Pearson correlation coefficient (PCC)":"0.62"},"uses_additional_data":false},{"leaderboard":"/sota/utterance-level-pronounciation-scoring-on","task":"Utterance-level pronounciation scoring","dataset":"speechocean762","model":"HiPAMA-Librispeech","rank_in_archive_order":3,"of":5,"metrics":{"Pearson correlation coefficient (PCC)":"0.754"},"uses_additional_data":false},{"leaderboard":"/sota/word-level-pronunciation-scoring-on","task":"Word-level pronunciation scoring","dataset":"speechocean762","model":"HiPAMA-Librispeech","rank_in_archive_order":4,"of":5,"metrics":{"Pearson correlation coefficient (PCC)":"0.59"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2211.08102","atlas_url":"https://app.syntology.ai/?focus=2211.08102","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}