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-22423@www.clsp.jhu.edu DTSTAMP:20240329T053708Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20221007T120000 DTEND;TZID=America/New_York:20221007T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Ariya Rastrow (Amazon) URL:https://www.clsp.jhu.edu/events/ariya-rastrow-amazon-2/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,October\,Rastrow END:VEVENT BEGIN:VEVENT UID:ai1ec-22403@www.clsp.jhu.edu DTSTAMP:20240329T053708Z 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 BEGIN:VEVENT UID:ai1ec-23312@www.clsp.jhu.edu DTSTAMP:20240329T053708Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nAdvanced neural language m odels have grown ever larger and more complex\, pushing forward the limits of language understanding and generation\, while diminishing interpretabi lity. The black-box nature of deep neural networks blocks humans from unde rstanding them\, as well as trusting and using them in real-world applicat ions. This talk will introduce interpretation techniques that bridge the g ap between humans and models for developing trustworthy natural language p rocessing
\n (NLP). I will first show how to explain black-box models and evaluate their explanations for understanding their p rediction behavior. Then I will introduce how to improve the interpretabil ity of neural language models by making their decision-making transparent and rationalized. Finally\, I will discuss how to diagnose and improve mod els (e.g.\, robustness) through the lens of explanations. I will conclude with future research directions that are centered around model interpretab ility and committed to facilitating communications and interactions betwee n intelligent machines\, system developers\, and end users for long-term t rustworthy AI.Biography
\nHanjie Chen is a Ph.D. candidate in Computer Science at the University of Virginia\, advis ed by Prof. Yangfeng Ji. Her research interests lie in Trustworthy AI\, Na tural Language Processing (NLP)\, and
DTSTART;TZID=America/New_York:20230313T120000 DTEND;TZID=America/New_York:20230313T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Hanjie Chen (University of Virginia) “Bridging Humans and Machines: Techniques for Trustworthy NLP” URL:https://www.clsp.jhu.edu/events/hanjie-chen-university-of-virginia/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Chen\,February END:VEVENT BEGIN:VEVENT UID:ai1ec-24509@www.clsp.jhu.edu DTSTAMP:20240329T053708Z 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 Interpretabl e Machine Learning. She develops interpretation techniques to explain neur al language models and make their prediction behavior transparent and reli able. She is a recipient of the Carlos and Esther Farrar Fellowship and th e Best Poster Award at the ACM CAPWIC 2021. Her work has been published at top-tier NLP/AI conferences (e.g.\, ACL\, AAAI\, EMNLP\, NAACL) and selec ted by the National Center for Women & Information Technology (NCWIT) Coll egiate Award Finalist 2021. She (as the primary instructor) co-designed an d taught the course\, Interpretable Machine Learning\, and was awarded the UVA CS Outstanding Graduate Teaching Award and University-wide Graduate T eaching Awards Nominee (top 5% of graduate instructors). More details can be found at https://www.cs.virginia.edu/~hc9mx