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:20240328T194830Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nNatural language processin g has been revolutionized by neural networks\, which perform impressively well in applications such as machine translation and question answering. D espite their success\, neural networks still have some substantial shortco mings: Their internal workings are poorly understood\, and they are notori ously brittle\, failing on example types that are rare in their training d ata. In this talk\, I will use the unifying thread of hierarchical syntact ic structure to discuss approaches for addressing these shortcomings. Firs t\, I will argue for a new evaluation paradigm based on targeted\, hypothe sis-driven tests that better illuminate what models have learned\; using t his paradigm\, I will show that even state-of-the-art models sometimes fai l to recognize the hierarchical 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 behavioral failings can be explained through analy sis of models’ inductive biases and internal representations\, focusing on the puzzle of how neural networks represent discrete symbolic structure i n continuous vector space. I will close by showing how insights from these analyses can be used to make models more robust through approaches based on meta-learning\, structured architectures\, and data augmentation.
\nBiography
\nTom McCoy is a PhD candidate in the Department of Cognitive Science at Johns Hopkins University. As an undergr aduate\, he studied computational linguistics at Yale. His research combin es natural language processing\, 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 lin guistic structure\, since these are two of the major components that deter mine 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-TAGS;LANGUAGE=en-US:2022\,January\,McCoy END:VEVENT BEGIN:VEVENT UID:ai1ec-22403@www.clsp.jhu.edu DTSTAMP:20240328T194830Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nVoice conversion (VC) is a significant aspect of artificial intelligence. It is the study of how to convert one’s voice to sound like that of another without changing the lin guistic content. Voice conversion belongs to a general technical field of speech synthesis\, which converts text to speech or changes the properties of speech\, for example\, voice identity\, emotion\, and accents. Voice c onversion involves multiple speech processing techniques\, such as speech analysis\, spectral conversion\, prosody conversion\, speaker characteriza tion\, and vocoding. With the recent advances in theory and practice\, we are now able to produce human-like voice quality with high speaker similar ity. In this talk\, Dr. Sisman will present the recent advances in voice c onversion and discuss their promise and limitations. Dr. Sisman will also provide a summary of the available resources for expressive voice conversi on research.
\nBiography
\nDr. Berrak Sisman (Member\, IEEE) received the Ph.D. degree in electrical and computer engin eering from National University of Singapore in 2020\, fully funded by A*S TAR Graduate Academy under Singapore International Graduate Award (SINGA). She is currently working as a tenure-track Assistant Professor at the Eri k Jonsson School Department of Electrical and Computer Engineering at Univ ersity of Texas at Dallas\, United States. Prior to joining UT Dallas\, sh e was a faculty member at Singapore University of Technology and Design (2 020-2022). She was a Postdoctoral Research Fellow at the National Universi ty of Singapore (2019-2020). She was an exchange doctoral student at the U niversity of Edinburgh and a visiting scholar at The Centre for Speech Tec hnology Research (CSTR)\, University of Edinburgh (2019). She was a visiti ng researcher at RIKEN Advanced Intelligence Project in Japan (2018). Her research is focused on machine learning\, signal processing\, emotion\, sp eech synthesis and voice conversion.
\nDr. Sisman has served as the Area Chair at INTERSPEECH 2021\, INTERSPEECH 2022\, IEEE SLT 2022 and as t he Publication Chair at ICASSP 2022. She has been elected as a member of t he IEEE Speech and Language Processing Technical Committee (SLTC) in the a rea of Speech Synthesis for the term from January 2022 to December 2024. S he plays leadership roles in conference organizations and active in techni cal committees. She has served as the General Coordinator of the Student A dvisory Committee (SAC) of International Speech Communication Association (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-TAGS;LANGUAGE=en-US:2022\,November\,Sisman END:VEVENT END:VCALENDAR