{"title":"VLSP 2021 - ASR Challenge for Vietnamese Automatic Speech Recognition","authors":"Van Hai Do","doi":"10.25073/2588-1086/vnucsce.356","DOIUrl":null,"url":null,"abstract":"Recently, Vietnamese speech recognition has been attracted by various research groups in both academics and industry. This paper presents a Vietnamese automatic speech recognition challenge for the eighth annual workshop on Vietnamese Language and Speech Processing (VLSP 2021). There are two sub-tasks in the challenge. The first task is ASR-Task1 focusing on a full pipeline development of the ASR model from scratch with both labeled and unlabeled training data provided by the organizer. The second task is ASR-Task2 focusing on spontaneous speech in different real scenarios e.g., meeting conversation, lecture speech. In the ASR-Task2, participants can use all available data sources to develop their models without any limitations. The quality of the models is evaluated by the Syllable Error Rate (SyER) metric.","PeriodicalId":416488,"journal":{"name":"VNU Journal of Science: Computer Science and Communication Engineering","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"VNU Journal of Science: Computer Science and Communication Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25073/2588-1086/vnucsce.356","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Recently, Vietnamese speech recognition has been attracted by various research groups in both academics and industry. This paper presents a Vietnamese automatic speech recognition challenge for the eighth annual workshop on Vietnamese Language and Speech Processing (VLSP 2021). There are two sub-tasks in the challenge. The first task is ASR-Task1 focusing on a full pipeline development of the ASR model from scratch with both labeled and unlabeled training data provided by the organizer. The second task is ASR-Task2 focusing on spontaneous speech in different real scenarios e.g., meeting conversation, lecture speech. In the ASR-Task2, participants can use all available data sources to develop their models without any limitations. The quality of the models is evaluated by the Syllable Error Rate (SyER) metric.