{"title":"The IBM keyword search system for the DARPA RATS program","authors":"L. Mangu, H. Soltau, H. Kuo, G. Saon","doi":"10.1109/ASRU.2013.6707730","DOIUrl":null,"url":null,"abstract":"The paper describes a state-of-the-art keyword search (KWS) system in which significant improvements are obtained by using Convolutional Neural Network acoustic models, a two-step speech segmentation approach and a simplified ASR architecture optimized for KWS. The system described in this paper had the best performance in the 2013 DARPA RATS evaluation for both Levantine and Farsi.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASRU.2013.6707730","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The paper describes a state-of-the-art keyword search (KWS) system in which significant improvements are obtained by using Convolutional Neural Network acoustic models, a two-step speech segmentation approach and a simplified ASR architecture optimized for KWS. The system described in this paper had the best performance in the 2013 DARPA RATS evaluation for both Levantine and Farsi.
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