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-20117@www.clsp.jhu.edu DTSTAMP:20240328T154359Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNeural sequence generation systems oftentimes generat e sequences by searching for the most likely sequence under the learnt pro bability distribution. This assumes that the most likely sequence\, i.e. t he mode\, under such a model must also be the best sequence it has to offe r (often in a given context\, e.g. conditioned on a source sentence in tra nslation). Recent findings in neural machine translation (NMT) show that t he true most likely sequence oftentimes is empty under many state-of-the-a rt NMT models. This follows a large list of other pathologies and biases o bserved in NMT and other sequence generation models: a length bias\, large r beams degrading performance\, exposure bias\, and many more. Many of the se works blame the probabilistic formulation of NMT or maximum likelihood estimation. We provide a different view on this: it is mode-seeking search \, e.g. beam search\, that introduces many of these pathologies and biases \, and such a decision rule is not suitable for the type of distributions learnt by NMT systems. We show that NMT models spread probability mass ove r many translations\, and that the most likely translation oftentimes is a rare event. We further show that translation distributions do capture imp ortant aspects of translation well in expectation. Therefore\, we advocate for decision rules that take into account the entire probability distribu tion and not just its mode. We provide one example of such a decision rule \, and show that this is a fruitful research direction.\nBiography\nI am a n assistant professor (UD) in natural language processing at the Institute for Logic\, Language and Computation where I lead the Probabilistic Langu age Learning group.\nMy work concerns the design of models and algorithms that learn to represent\, understand\, and generate language data. Example s of specific problems I am interested in include language modelling\, mac hine translation\, syntactic parsing\, textual entailment\, text classific ation\, and question answering.\nI also develop techniques to approach gen eral machine learning problems such as probabilistic inference\, gradient and density estimation.\nMy interests sit at the intersection of disciplin es such as statistics\, machine learning\, approximate inference\, global optimization\, formal languages\, and computational linguistics.\n \n DTSTART;TZID=America/New_York:20210419T120000 DTEND;TZID=America/New_York:20210419T131500 LOCATION:via Zoom SEQUENCE:0 SUMMARY:Wilker Aziz (University of Amsterdam) “The Inadequacy of the Mode in Neural Machine Translation” URL:https://www.clsp.jhu.edu/events/wilker-aziz-university-of-amsterdam/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nNeural sequence generation systems oftentimes generat e sequences by searching for the most likely sequence under the learnt pro bability distribution. This assumes that the most likely sequence\, i.e. t he mode\, under such a model must also be the best sequence it has to offe r (often in a given context\, e.g. conditioned on a source sentence in tra nslation). Recent findings in neural machine translation (NMT) show that t he true most likely sequence oftentimes is empty under many state-of-the-a rt NMT models. This follows a large list of other pathologies and biases o bserved in NMT and other sequence generation models: a length bias\, large r beams degrading performance\, exposure bias\, and many more. Many of the se works blame the probabilistic formulation of NMT or maximum likelihood estimation. We provide a different view on this: it is mode-seeking search \, e.g. beam search\, that introduces many of these pathologies and biases \, and such a decision rule is not suitable for the type of distributions learnt by NMT systems. We show that NMT models spread probability mass ove r many translations\, and that the most likely translation oftentimes is a rare event. We further show that translation distributions do capture imp ortant aspects of translation well in expectation. Therefore\, we advocate for decision rules that take into account the entire probability distribu tion and not just its mode. We provide one example of such a decision rule \, and show that this is a fruitful research direction.
\nBi ography
\nI am an assistant professor (UD) in natu ral language processing at the Institute for Logic\, Language and Computation where I lead the Probabilistic Language Learning group.
\nMy work concerns the design of models and algorithms that learn to represe nt\, understand\, and generate language data. Examples of specific problem s I am interested in include language modelling\, machine translation\, sy ntactic parsing\, textual entailment\, text classification\, and question answering.
\nI also develop techniques to approach general machine l earning problems such as probabilistic inference\, gradient and density es timation.
\nMy interests sit at the intersection of disciplines such as statistics\, machine learning\, approximate inference\, global optimiz ation\, formal languages\, and computational linguistics.
\n\n< p> \n X-TAGS;LANGUAGE=en-US:2021\,April\,Aziz END:VEVENT BEGIN:VEVENT UID:ai1ec-23320@www.clsp.jhu.edu DTSTAMP:20240328T154359Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSpeech communications represents a core domain for ed ucation\, team problem solving\, social engagement\, and business interact ions. The ability for Speech Technology to extract layers of knowledge and assess engagement content represents the next generation of advanced spee ch solutions. Today\, the emergence of BIG DATA\, Machine Learning\, as we ll as voice enabled speech systems have required the need for effective vo ice capture and automatic speech/speaker recognition. The ability to emplo y speech and language technology to assess human-to-human interactions off ers new research paradigms having profound impact on assessing human inter action. In this talk\, we will focus on big data naturalistic audio proces sing relating to (i) child learning spaces\, and (ii) the NASA APOLLO luna r missions. ML based technology advancements include automatic audio diari zation\, speech recognition\, and speaker recognition. Child-Teacher based assessment of conversational interactions are explored\, including keywor d and “WH-word” (e.g.\, who\, what\, etc.). Diarization processing solutio ns are applied to both classroom/learning space child speech\, as well as massive APOLLO data. CRSS-UTDallas is expanding our original Apollo-11 cor pus\, resulting in a massive multi-track audio processing challenge to mak e available 150\,000hrs of Apollo mission data to be shared with science c ommunities: (i) speech/language technology\, (ii) STEM/science and team-ba sed researchers\, and (iii) education/historical/archiving specialists. Ou r goals here are to provide resources which allow to better understand how people work/learn collaboratively together. For Apollo\, to accomplish on e of mankind’s greatest scientific/technological challenges in the last ce ntury.\nBiography\nJohn H.L. Hansen\, received Ph.D. & M.S. degrees from G eorgia Institute of Technology\, and B.S.E.E. from Rutgers Univ. He joined Univ. of Texas at Dallas (UTDallas) in 2005\, where he currently serves a s Associate Dean for Research\, Prof. of ECE\, Distinguished Univ. Chair i n Telecom. Engineering\, and directs Center for Robust Speech Systems (CRS S). He is an ISCA Fellow\, IEEE Fellow\, and has served as Member and TC-C hair of IEEE Signal Proc. Society\, Speech & Language Proc. Tech. Comm.(SL TC)\, and Technical Advisor to U.S. Delegate for NATO (IST/TG-01). He serv ed as ISCA President (2017-21)\, continues to serve on ISCA Board (2015-23 ) as Treasurer\, has supervised 99 PhD/MS thesis candidates (EE\,CE\,BME\, TE\,CS\,Ling.\,Cog.Sci.\,Spch.Sci.\,Hear.Sci)\, was recipient of 2020 UT-D allas Provost’s Award for Grad. PhD Research Mentoring\; author/co-author of 865 journal/conference papers including 14 textbooks in the field of sp eech/language/hearing processing & technology including coauthor of textbo ok Discrete-Time Processing of Speech Signals\, (IEEE Press\, 2000)\, and lead author of the report “The Impact of Speech Under ‘Stress’ on Military Speech Technology\,” (NATO RTO-TR-10\, 2000). He served as Organizer\, Ch air/Co-Chair/Tech.Chair for ISCA INTERSPEECH-2022\, IEEE ICASSP-2010\, IEE E SLT-2014\, ISCA INTERSPEECH-2002\, and Tech. Chair for IEEE ICASSP-2024. He received the 2022 IEEE Signal Processing Society Leo Beranek MERITORIO US SERVICE Award.\n DTSTART;TZID=America/New_York:20230303T120000 DTEND;TZID=America/New_York:20230303T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:John Hansen (University of Texas at Dallas) “Challenges and Advance ments in Speaker Diarization & Recognition for Naturalistic Data Streams” URL:https://www.clsp.jhu.edu/events/john-hansen-university-of-texas-at-dall as/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nSpeech communications represents a core domain for ed ucation\, team problem solving\, social engagement\, and business interact ions. The ability for Speech Technology to extract layers of knowledge and assess engagement content represents the next generation of advanced spee ch solutions. Today\, the emergence of BIG DATA\, Machine Learning\, as we ll as voice enabled speech systems have required the need for effective vo ice capture and automatic speech/speaker recognition. The ability to emplo y speech and language technology to assess human-to-human interactions off ers new research paradigms having profound impact on assessing human inter action. In this talk\, we will focus on big data naturalistic audio proces sing relating to (i) child learning spaces\, and (ii) the NASA APOLLO luna r missions. ML based technology advancements include automatic audio diari zation\, speech recognition\, and speaker recognition. Child-Teacher based assessment of conversational interactions are explored\, including keywor d and “WH-word” (e.g.\, who\, what\, etc.). Diarization processing solutio ns are applied to both classroom/learning space child speech\, as well as massive APOLLO data. CRSS-UTDallas is expanding our original Apollo-11 cor pus\, resulting in a massive multi-track audio processing challenge to mak e available 150\,000hrs of Apollo mission data to be shared with science c ommunities: (i) speech/language technology\, (ii) STEM/science and team-ba sed researchers\, and (iii) education/historical/archiving specialists. Ou r goals here are to provide resources which allow to better understand how people work/learn collaboratively together. For Apollo\, to accomplish on e of mankind’s greatest scientific/technological challenges in the last ce ntury.
\nBiography
\nJohn H.L. Hansen\, recei ved Ph.D. & M.S. degrees from Georgia Institute of Technology\, and B.S.E. E. from Rutgers Univ. He joined Univ. of Texas at Dallas (UTDallas) in 200 5\, where he currently serves as Associate Dean for Research\, Prof. of EC E\, Distinguished Univ. Chair in Telecom. Engineering\, and directs Center for Robust Speech Systems (CRSS). He is an ISCA Fellow\, IEEE Fellow\, an d has served as Member and TC-Chair of IEEE Signal Proc. Society\, Speech & Language Proc. Tech. Comm.(SLTC)\, and Technical Advisor to U.S. Delegat e for NATO (IST/TG-01). He served as ISCA President (2017-21)\, continues to serve on ISCA Board (2015-23) as Treasurer\, has supervised 99 PhD/MS t hesis candidates (EE\,CE\,BME\,TE\,CS\,Ling.\,Cog.Sci.\,Spch.Sci.\,Hear.Sc i)\, was recipient of 2020 UT-Dallas Provost’s Award for Grad. PhD Researc h Mentoring\; author/co-author of 865 journal/conference papers including 14 textbooks in the field of speech/language/hearing processing & technolo gy including coauthor of textbook Discrete-Time Processing of Speech Signa ls\, (IEEE Press\, 2000)\, and lead author of the report “The Impact of Sp eech Under ‘Stress’ on Military Speech Technology\,” (NATO RTO-TR-10\, 200 0). He served as Organizer\, Chair/Co-Chair/Tech.Chair for ISCA INTERSPEEC H-2022\, IEEE ICASSP-2010\, IEEE SLT-2014\, ISCA INTERSPEECH-2002\, and Te ch. Chair for IEEE ICASSP-2024. He received the 2022 IEEE Signal Processin g Society Leo Beranek MERITORIOUS SERVICE Award.
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