{"title":"Utterance verification using garbage words for a hospital appointment system with speech interface","authors":"Mitsuru Takaoka, H. Nishizaki, Y. Sekiguchi","doi":"10.1109/ASRU.2011.6163954","DOIUrl":null,"url":null,"abstract":"On a system that captures spoken dialog, users often use out-of-domain utterances to the system. The speech recognition component in the dialog system cannot correctly recognize such utterances, which causes fatal errors. This paper proposes a method to verify whether utterances are in-domain or out-of-domain. The proposed method trains systems with two language models: one that can accept both in-domain and out-of-domain utterances and the other that can accept only in-domain utterances. These models are installed into two speech recognition systems. A comparison of the recognizers' outputs provides a good verification of utterances. We installed our method in a hospital appointment system and evaluated it. The experimental results showed that the proposed method worked well.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASRU.2011.6163954","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
On a system that captures spoken dialog, users often use out-of-domain utterances to the system. The speech recognition component in the dialog system cannot correctly recognize such utterances, which causes fatal errors. This paper proposes a method to verify whether utterances are in-domain or out-of-domain. The proposed method trains systems with two language models: one that can accept both in-domain and out-of-domain utterances and the other that can accept only in-domain utterances. These models are installed into two speech recognition systems. A comparison of the recognizers' outputs provides a good verification of utterances. We installed our method in a hospital appointment system and evaluated it. The experimental results showed that the proposed method worked well.