Research on Transformer-Based Mongolian G2P Conversion

Research on Transformer-Based Mongolian G2P Conversion | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 July 2025 V1 Latest version Share on Research on Transformer-Based Mongolian G2P Conversion Authors : ShunYi and 2ndSaRengaowa [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175146128.81774096/v1 163 views 102 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The Transformer architecture has made breakthroughs in the field of natural language processing, and pre-trained models represented by BERT and GPT have demonstrated excellent performance. Aiming at the problem that the generalization ability of statistical G2P models is limited in the low-resource Mongolian language environment, this study proposes an end-to-end Mongolian grapheme-phoneme conversion model based on the Transformer architecture. The model effectively models the contextual dependency of Mongolian characters through the hierarchical representation learning of the multi-head self-attention mechanism, improving the robustness of conversion. In response to the scarcity of annotated Mongolian data, this study constructs a grapheme-phoneme aligned corpus containing 25,000 entries. Experiments show that compared with the baseline model (Sequitur G2P), this model achieves a 5.6% reduction in the WER index. Further hyperparameter analysis reveals that the collaborative optimization of the intermediate layer dimension of the feed-forward layer network and the number of attention heads has a significant impact on model performance. This study makes contributions in the following three aspects: (1) It is the first to apply the Transformer architecture to the Mongolian G2P task; (2) It constructs a Mongolian grapheme-phoneme aligned corpus, providing data support for low-resource Mongolian language research; (3) It systematically evaluates the influence rules of model hyperparameters on performance, providing an experimental benchmark for follow-up research. Information & Authors Information Version history V1 Version 1 02 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords attention mechanism grapheme-to-phoneme conversion neural network Authors Affiliations ShunYi Inner Mongolia Normal University View all articles by this author 2ndSaRengaowa [email protected] Inner Mongolia Normal University View all articles by this author Metrics & Citations Metrics Article Usage 163 views 102 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation ShunYi, 2ndSaRengaowa. Research on Transformer-Based Mongolian G2P Conversion. Authorea . 02 July 2025. DOI: https://doi.org/10.22541/au.175146128.81774096/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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