BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-20730@www.clsp.jhu.edu DTSTAMP:20240328T125653Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRaytheon BBN participated in the IARPA MATERIAL progr am\, whose objective is to enable rapid development of language-independen t methods for cross-lingual information retrieval (CLIR). The challenging CLIR task of retrieving documents written (or spoken) in one language so t hat they satisfy an information need expressed in a different language is exacerbated by unique challenges posed by the MATERIAL program: limited tr aining data for automatic speech recognition and machine translation\, sca nt lexical resources\, non-standardized orthography\, etc. Furthermore\, t he format of the queries and the “Query-Weighted Value” performance measur e are non-standard and not previously studied in the IR community. In this talk\, we will describe the Raytheon BBN CLIR system\, which was successf ul at addressing the above challenges and unique characteristics of the pr ogram.\nBiography\n\nDamianos Karakos has been at Raytheon BBN for the pas t nine years\, where he is currently a Senior Principal Engineer\, Researc h. Before that\, he was research faculty at Johns Hopkins University. He h as worked on several Government projects (e.g.\, DARPA GALE\, DARPA RATS\, IARPA BABEL\, IARPA MATERIAL\, IARPA BETTER) and on a variety of HLT-rela ted topics (e.g.\, speech recognition\, speech activity detection\, keywor d search\, information retrieval). He has published more than 60 peer-revi ewed papers. His research interests lie at the intersection of human langu age technology and machine learning\, with an emphasis on statistical meth ods. He obtained a PhD in Electrical Engineering from the University of Ma ryland\, College Park\, in 2002. DTSTART;TZID=America/New_York:20210924T120000 DTEND;TZID=America/New_York:20210924T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Damianos Karakos (Raytheon BBN) “The Raytheon BBN Cross-lingual Inf ormation Retrieval System developed under the IARPA MATERIAL Program” URL:https://www.clsp.jhu.edu/events/damianos-karakos/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nRaytheon BBN participated in the IARPA MATERIAL progr am\, whose objective is to enable rapid development of language-independen t methods for cross-lingual information retrieval (CLIR). The challenging CLIR task of retrieving documents written (or spoken) in one language so t hat they satisfy an information need expressed in a different language is exacerbated by unique challenges posed by the MATERIAL program: limited tr aining data for automatic speech recognition and machine translation\, sca nt lexical resources\, non-standardized orthography\, etc. Furthermore\, t he format of the queries and the “Query-Weighted Value” performance measur e are non-standard and not previously studied in the IR community. In this talk\, we will describe the Raytheon BBN CLIR system\, which was successf ul at addressing the above challenges and unique characteristics of the pr ogram.
\nBiography
\nDamianos Karakos has been at Raytheon BBN for the past nine years\, wh ere he is currently a Senior Principal Engineer\, Research. Before that\, he was research faculty at Johns Hopkins University. He has worked on seve ral Government projects (e.g.\, DARPA GALE\, DARPA RATS\, IARPA BABEL\, IA RPA MATERIAL\, IARPA BETTER) and on a variety of HLT-related topics (e.g.\ , speech recognition\, speech activity detection\, keyword search\, inform ation retrieval). He has published more than 60 peer-reviewed papers. His research interests lie at the intersection of human language technology an d machine learning\, with an emphasis on statistical methods. He obtained a PhD in Electrical Engineering from the University of Maryland\, College Park\, in 2002.
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Abstr act
\nOne of the keys to success in machine learning applic ations is to improve each user’s personal experience via personalized mode ls. A personalized model can be a more resource-efficient solution than a general-purpose model\, too\, because it focuses on a particular sub-probl em\, for which a smaller model architecture can be good enough. However\, training a personalized model requires data from the particular test-time user\, which are not always available due to their private nature and tech nical challenges. Furthermore\, such data tend to be unlabeled as they can be collected only during the test time\, once after the system is deploye d to user devices. One could rely on the generalization power of a generic model\, but such a model can be too computationally/spatially complex for real-time processing in a resource-constrained device. In this talk\, I will present some techniques to circumvent the lack of labeled personal data in the context of speech enhancement. Ou r machine learning models will require zero or few data samples from the t est-time users\, while they can still achieve the personalization goal. To this end\, we will investigate modularized speech enhancement models as w ell as the potential of self-supervised learning for personalized speech e nhancement. Because our research achieves the personalization goal in a da ta- and resource-efficient way\, it is a step towards a more available and affordable AI for society.
\nBiography
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