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-22375@www.clsp.jhu.edu DTSTAMP:20240329T091543Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nI will present our work on data augmentation using style transfer as a way to im prove domain adaptation in sequence labeling tasks. The target domain is s ocial media data\, and the task is named entity recognition (NER). The pre mise is that we can transform the labelled out of domain data into somethi ng that stylistically is more closely related to the target data. Then we can train a model on a combination of the generated data and the smaller a mount of in domain data to improve NER prediction performance. I will show recent empirical results on these efforts.
\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUAL (Research in Text Understanding and Analysis of Language) lab. The common thread among all these research problems is t he scarcity of labeled data.
\nBiography
\nThamar Solorio is a Professor of Com puter Science at the University of Houston (UH). She holds graduate degree s in Computer Science from the Instituto Nacional de Astrofísica\, Óptica y Electrónica\, in Puebla\, Mexico. Her research interests include informa tion extraction from social media data\, enabling technology for code-swit ched data\, stylistic modeling of text\, and more recently multimodal appr oaches for online content understanding. She is the director and founder o f the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for he r work on authorship attribution\, and recipient of the 2014 Emerging Lead er ABIE Award in Honor of Denice Denton. She is currently serving a second term as an elected board member of the North American Chapter of the Asso ciation of Computational Linguistics and was PC co-chair for NAACL 2019. S he recently joined the team of Editors in Chief for the ACL Rolling Review (ARR) system. Her research is currently funded by the NSF and by ADOBE. p> DTSTART;TZID=America/New_York:20220923T120000 DTEND;TZID=America/New_York:20220923T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Thamar Solorio (University of Houston) “Style Transfer for Data Aug mentation in Sequence Labeling Tasks” URL:https://www.clsp.jhu.edu/events/thamar-solorio-university-of-houston-st yle-transfer-for-data-augmentation-in-sequence-labeling-tasks/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,September\,Solorio END:VEVENT BEGIN:VEVENT UID:ai1ec-22403@www.clsp.jhu.edu DTSTAMP:20240329T091543Z 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-24509@www.clsp.jhu.edu DTSTAMP:20240329T091543Z 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