D. Karakos, R. Schwartz, S. Tsakalidis, Le Zhang, Shivesh Ranjan, Tim Ng, Roger Hsiao, G. Saikumar, I. Bulyko, L. Nguyen, J. Makhoul, F. Grézl, M. Hannemann, M. Karafiát, Igor Szöke, Karel Veselý, L. Lamel, V. Le
{"title":"Score normalization and system combination for improved keyword spotting","authors":"D. Karakos, R. Schwartz, S. Tsakalidis, Le Zhang, Shivesh Ranjan, Tim Ng, Roger Hsiao, G. Saikumar, I. Bulyko, L. Nguyen, J. Makhoul, F. Grézl, M. Hannemann, M. Karafiát, Igor Szöke, Karel Veselý, L. Lamel, V. Le","doi":"10.1109/ASRU.2013.6707731","DOIUrl":null,"url":null,"abstract":"We present two techniques that are shown to yield improved Keyword Spotting (KWS) performance when using the ATWV/MTWV performance measures: (i) score normalization, where the scores of different keywords become commensurate with each other and they more closely correspond to the probability of being correct than raw posteriors; and (ii) system combination, where the detections of multiple systems are merged together, and their scores are interpolated with weights which are optimized using MTWV as the maximization criterion. Both score normalization and system combination approaches show that significant gains in ATWV/MTWV can be obtained, sometimes on the order of 8-10 points (absolute), in five different languages. A variant of these methods resulted in the highest performance for the official surprise language evaluation for the IARPA-funded Babel project in April 2013.","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":"104","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.2013.6707731","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 104
Abstract
We present two techniques that are shown to yield improved Keyword Spotting (KWS) performance when using the ATWV/MTWV performance measures: (i) score normalization, where the scores of different keywords become commensurate with each other and they more closely correspond to the probability of being correct than raw posteriors; and (ii) system combination, where the detections of multiple systems are merged together, and their scores are interpolated with weights which are optimized using MTWV as the maximization criterion. Both score normalization and system combination approaches show that significant gains in ATWV/MTWV can be obtained, sometimes on the order of 8-10 points (absolute), in five different languages. A variant of these methods resulted in the highest performance for the official surprise language evaluation for the IARPA-funded Babel project in April 2013.
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