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-21259@www.clsp.jhu.edu DTSTAMP:20240328T202957Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNatural language processing has been revolutionized b y neural networks\, which perform impressively well in applications such a s machine translation and question answering. Despite their success\, neur al networks still have some substantial shortcomings: Their internal worki ngs are poorly understood\, and they are notoriously brittle\, failing on example types that are rare in their training data. In this talk\, I will use the unifying thread of hierarchical syntactic structure to discuss app roaches for addressing these shortcomings. First\, I will argue for a new evaluation paradigm based on targeted\, hypothesis-driven tests that bette r illuminate what models have learned\; using this paradigm\, I will show that even state-of-the-art models sometimes fail to recognize the hierarch ical structure of language (e.g.\, to conclude that “The book on the table is blue” implies “The table is blue.”) Second\, I will show how these beh avioral failings can be explained through analysis of models’ inductive bi ases and internal representations\, focusing on the puzzle of how neural n etworks represent discrete symbolic structure in continuous vector space. I will close by showing how insights from these analyses can be used to ma ke models more robust through approaches based on meta-learning\, structur ed architectures\, and data augmentation.\nBiography\nTom McCoy is a PhD c andidate in the Department of Cognitive Science at Johns Hopkins Universit y. As an undergraduate\, he studied computational linguistics at Yale. His research combines natural language processing\, cognitive science\, and m achine learning to study how we can achieve robust generalization in model s of language\, as this remains one of the main areas where current AI sys tems fall short. In particular\, he focuses on inductive biases and repres entations of linguistic structure\, since these are two of the major compo nents that determine how learners generalize to novel types of input. DTSTART;TZID=America/New_York:20220131T120000 DTEND;TZID=America/New_York:20220131T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Tom McCoy (Johns Hopkins University) “Opening the Black Box of Deep Learning: Representations\, Inductive Biases\, and Robustness” URL:https://www.clsp.jhu.edu/events/tom-mccoy-johns-hopkins-university-open ing-the-black-box-of-deep-learning-representations-inductive-biases-and-ro bustness/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nNatural language processing has been revolutionized b y neural networks\, which perform impressively well in applications such a s machine translation and question answering. Despite their success\, neur al networks still have some substantial shortcomings: Their internal worki ngs are poorly understood\, and they are notoriously brittle\, failing on example types that are rare in their training data. In this talk\, I will use the unifying thread of hierarchical syntactic structure to discuss app roaches for addressing these shortcomings. First\, I will argue for a new evaluation paradigm based on targeted\, hypothesis-driven tests that bette r illuminate what models have learned\; using this paradigm\, I will show that even state-of-the-art models sometimes fail to recognize the hierarch ical structure of language (e.g.\, to conclude that “The book on the table is blue” implies “The table is blue.”) Second\, I will show how these beh avioral failings can be explained through analysis of models’ inductive bi ases and internal representations\, focusing on the puzzle of how neural n etworks represent discrete symbolic structure in continuous vector space. I will close by showing how insights from these analyses can be used to ma ke models more robust through approaches based on meta-learning\, structur ed architectures\, and data augmentation.
\nBiography
\nTom McCoy is a PhD candidate in the Department of Cognitive Sci ence at Johns Hopkins University. As an undergraduate\, he studied computa tional linguistics at Yale. His research combines natural language process ing\, cognitive science\, and machine learning to study how we can achieve robust generalization in models of language\, as this remains one of the main areas where current AI systems fall short. In particular\, he focuses on inductive biases and representations of linguistic structure\, since t hese are two of the major components that determine how learners generaliz e to novel types of input.
\n X-TAGS;LANGUAGE=en-US:2022\,January\,McCoy END:VEVENT BEGIN:VEVENT UID:ai1ec-22403@www.clsp.jhu.edu DTSTAMP:20240328T202957Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nVoice conversion (VC) is a significant aspect of arti ficial intelligence. It is the study of how to convert one’s voice to soun d like that of another without changing the linguistic content. Voice conv ersion belongs to a general technical field of speech synthesis\, which co nverts text to speech or changes the properties of speech\, for example\, voice identity\, emotion\, and accents. Voice conversion involves multiple speech processing techniques\, such as speech analysis\, spectral convers ion\, prosody conversion\, speaker characterization\, and vocoding. With t he recent advances in theory and practice\, we are now able to produce hum an-like voice quality with high speaker similarity. In this talk\, Dr. Sis man will present the recent advances in voice conversion and discuss their promise and limitations. Dr. Sisman will also provide a summary of the av ailable resources for expressive voice conversion research.\nBiography\nDr . Berrak Sisman (Member\, IEEE) received the Ph.D. degree in electrical an d computer engineering from National University of Singapore in 2020\, ful ly funded by A*STAR Graduate Academy under Singapore International Graduat e Award (SINGA). She is currently working as a tenure-track Assistant Prof essor at the Erik Jonsson School Department of Electrical and Computer Eng ineering at University of Texas at Dallas\, United States. Prior to joinin g UT Dallas\, she was a faculty member at Singapore University of Technolo gy and Design (2020-2022). She was a Postdoctoral Research Fellow at the N ational University of Singapore (2019-2020). She was an exchange doctoral student at the University of Edinburgh and a visiting scholar at The Centr e for Speech Technology Research (CSTR)\, University of Edinburgh (2019). She was a visiting researcher at RIKEN Advanced Intelligence Project in Ja pan (2018). Her research is focused on machine learning\, signal processin g\, emotion\, speech synthesis and voice conversion.\nDr. Sisman has serve d as the Area Chair at INTERSPEECH 2021\, INTERSPEECH 2022\, IEEE SLT 2022 and as the Publication Chair at ICASSP 2022. She has been elected as a me mber of the IEEE Speech and Language Processing Technical Committee (SLTC) in the area of Speech Synthesis for the term from January 2022 to Decembe r 2024. She plays leadership roles in conference organizations and active in technical committees. She has served as the General Coordinator of the Student Advisory Committee (SAC) of International Speech Communication Ass ociation (ISCA). DTSTART;TZID=America/New_York:20221104T120000 DTEND;TZID=America/New_York:20221104T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Berrak Sisman (University of Texas at Dallas) “Speech Synthesis and Voice Conversion: Machine Learning can Mimic Anyone’s Voice” URL:https://www.clsp.jhu.edu/events/berrak-sisman-university-of-texas-at-da llas/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nVoice conversion (VC) is a significant aspect of arti ficial intelligence. It is the study of how to convert one’s voice to soun d like that of another without changing the linguistic content. Voice conv ersion belongs to a general technical field of speech synthesis\, which co nverts text to speech or changes the properties of speech\, for example\, voice identity\, emotion\, and accents. Voice conversion involves multiple speech processing techniques\, such as speech analysis\, spectral convers ion\, prosody conversion\, speaker characterization\, and vocoding. With t he recent advances in theory and practice\, we are now able to produce hum an-like voice quality with high speaker similarity. In this talk\, Dr. Sis man will present the recent advances in voice conversion and discuss their promise and limitations. Dr. Sisman will also provide a summary of the av ailable resources for expressive voice conversion research.
\nDr. Berrak Sisman (Member\, IEEE) received th e Ph.D. degree in electrical and computer engineering from National Univer sity of Singapore in 2020\, fully funded by A*STAR Graduate Academy under Singapore International Graduate Award (SINGA). She is currently working a s a tenure-track Assistant Professor at the Erik Jonsson School Department of Electrical and Computer Engineering at University of Texas at Dallas\, United States. Prior to joining UT Dallas\, she was a faculty member at S ingapore University of Technology and Design (2020-2022). She was a Postdo ctoral Research Fellow at the National University of Singapore (2019-2020) . She was an exchange doctoral student at the University of Edinburgh and a visiting scholar at The Centre for Speech Technology Research (CSTR)\, U niversity of Edinburgh (2019). She was a visiting researcher at RIKEN Adva nced Intelligence Project in Japan (2018). Her research is focused on mach ine learning\, signal processing\, emotion\, speech synthesis and voice co nversion.
\nDr. Sisman has served as the Area Chair at INTERSPEECH 2 021\, INTERSPEECH 2022\, IEEE SLT 2022 and as the Publication Chair at ICA SSP 2022. She has been elected as a member of the IEEE Speech and Language Processing Technical Committee (SLTC) in the area of Speech Synthesis for the term from January 2022 to December 2024. She plays leadership roles i n conference organizations and active in technical committees. She has ser ved as the General Coordinator of the Student Advisory Committee (SAC) of International Speech Communication Association (ISCA).
\n X-TAGS;LANGUAGE=en-US:2022\,November\,Sisman END:VEVENT BEGIN:VEVENT UID:ai1ec-24509@www.clsp.jhu.edu DTSTAMP:20240328T202957Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20240408T120000 DTEND;TZID=America/New_York:20240408T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Berrak Sisman URL:https://www.clsp.jhu.edu/events/berrak-sisman/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,April\,Sisman END:VEVENT END:VCALENDAR