基于转换和块合并的越南语语音自动识别的大写恢复

H. P. T. Thu, B. N. Thai, V. H. Nguyen, Quoc Truong Do, Luong Chi Mai, Huyen Thi Minh Nguyen
{"title":"基于转换和块合并的越南语语音自动识别的大写恢复","authors":"H. P. T. Thu, B. N. Thai, V. H. Nguyen, Quoc Truong Do, Luong Chi Mai, Huyen Thi Minh Nguyen","doi":"10.1109/KSE.2019.8919342","DOIUrl":null,"url":null,"abstract":"In the last few years, Automatic Speech Recognition (ASR) systems for Vietnamese are utilized in various applications with exceptional results. Nevertheless, such ASR output still contains limitations such as the absence of punctuation, capitalization and standardize numeric data. These shortcomings cause difficulties for readers to understand context efficiently and for Natural Language Processing (NLP) tasks to be well-performed. Capitalization is one of the most critical factors to enhance human readability, parsing, and Named Entity Recognition (NER). Additionally, Vietnamese ASR output has its own features comparing to English such as lisp words, local words, compound words, and homophone. In this paper, we propose a method to Recover Capitalization for long-speech ASR transcription of Vietnamese using Transformer models and chunk merging. Furthermore, we perform decoding in parallel while improving the prediction accuracy.","PeriodicalId":439841,"journal":{"name":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Recovering Capitalization for Automatic Speech Recognition of Vietnamese using Transformer and Chunk Merging\",\"authors\":\"H. P. T. Thu, B. N. Thai, V. H. Nguyen, Quoc Truong Do, Luong Chi Mai, Huyen Thi Minh Nguyen\",\"doi\":\"10.1109/KSE.2019.8919342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the last few years, Automatic Speech Recognition (ASR) systems for Vietnamese are utilized in various applications with exceptional results. Nevertheless, such ASR output still contains limitations such as the absence of punctuation, capitalization and standardize numeric data. These shortcomings cause difficulties for readers to understand context efficiently and for Natural Language Processing (NLP) tasks to be well-performed. Capitalization is one of the most critical factors to enhance human readability, parsing, and Named Entity Recognition (NER). Additionally, Vietnamese ASR output has its own features comparing to English such as lisp words, local words, compound words, and homophone. In this paper, we propose a method to Recover Capitalization for long-speech ASR transcription of Vietnamese using Transformer models and chunk merging. Furthermore, we perform decoding in parallel while improving the prediction accuracy.\",\"PeriodicalId\":439841,\"journal\":{\"name\":\"2019 11th International Conference on Knowledge and Systems Engineering (KSE)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 11th International Conference on Knowledge and Systems Engineering (KSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KSE.2019.8919342\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 11th International Conference on Knowledge and Systems Engineering (KSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KSE.2019.8919342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

在过去的几年里,越南语自动语音识别(ASR)系统在各种应用中得到了出色的应用。然而,这样的ASR输出仍然存在诸如缺少标点符号、大写和标准化数字数据等限制。这些缺点导致读者难以有效地理解上下文,也影响了自然语言处理(NLP)任务的良好执行。大写是增强人类可读性、解析和命名实体识别(NER)的最关键因素之一。此外,越南语的ASR输出与英语相比有自己的特点,如口齿不清词、本地词、复合词和同音字。在本文中,我们提出了一种使用Transformer模型和块合并的方法来恢复越南语长语音ASR转录的大写。此外,我们在提高预测精度的同时进行并行解码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recovering Capitalization for Automatic Speech Recognition of Vietnamese using Transformer and Chunk Merging
In the last few years, Automatic Speech Recognition (ASR) systems for Vietnamese are utilized in various applications with exceptional results. Nevertheless, such ASR output still contains limitations such as the absence of punctuation, capitalization and standardize numeric data. These shortcomings cause difficulties for readers to understand context efficiently and for Natural Language Processing (NLP) tasks to be well-performed. Capitalization is one of the most critical factors to enhance human readability, parsing, and Named Entity Recognition (NER). Additionally, Vietnamese ASR output has its own features comparing to English such as lisp words, local words, compound words, and homophone. In this paper, we propose a method to Recover Capitalization for long-speech ASR transcription of Vietnamese using Transformer models and chunk merging. Furthermore, we perform decoding in parallel while improving the prediction accuracy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信