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-22403@www.clsp.jhu.edu DTSTAMP:20240328T233543Z 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-23312@www.clsp.jhu.edu DTSTAMP:20240328T233544Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAdvanced neural language models have grown ever large r and more complex\, pushing forward the limits of language understanding and generation\, while diminishing interpretability. The black-box nature of deep neural networks blocks humans from understanding them\, as well as trusting and using them in real-world applications. This talk will introd uce interpretation techniques that bridge the gap between humans and model s for developing trustworthy natural language processing(NLP). I will firs t show how to explain black-box models and evaluate their explanations for understanding their prediction behavior. Then I will introduce how to imp rove the interpretability of neural language models by making their decisi on-making transparent and rationalized. Finally\, I will discuss how to di agnose and improve models (e.g.\, robustness) through the lens of explanat ions. I will conclude with future research directions that are centered ar ound model interpretability and committed to facilitating communications a nd interactions between intelligent machines\, system developers\, and end users for long-term trustworthy AI.\nBiography\nHanjie Chen is a Ph.D. ca ndidate in Computer Science at the University of Virginia\, advised by Pro f. Yangfeng Ji. Her research interests lie in Trustworthy AI\, Natural Lan guage Processing (NLP)\, andInterpretable Machine Learning. She develops i nterpretation techniques to explain neural language models and make their prediction behavior transparent and reliable. She is a recipient of the Ca rlos and Esther Farrar Fellowship and the Best Poster Award at the ACM CAP WIC 2021. Her work has been published at top-tier NLP/AI conferences (e.g. \, ACL\, AAAI\, EMNLP\, NAACL) and selected by the National Center for Wom en & Information Technology (NCWIT) Collegiate Award Finalist 2021. She (a s the primary instructor) co-designed and taught the course\, Interpretabl e Machine Learning\, and was awarded the UVA CS Outstanding Graduate Teach ing Award and University-wide Graduate Teaching Awards Nominee (top 5% of graduate instructors). More details can be found athttps://www.cs.virginia .edu/~hc9mx 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-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAdvanced neural language models have grown ever large
r and more complex\, pushing forward the limits of language understanding
and generation\, while diminishing interpretability. The black-box nature
of deep neural networks blocks humans from understanding them\, as well as
trusting and using them in real-world applications. This talk will introd
uce interpretation techniques that bridge the gap between humans and model
s for developing trustworthy natural language processing
(NLP). I will first show how to explain black-box models and evalua
te their explanations for understanding their prediction behavior. Then I
will introduce how to improve the interpretability of neural language mode
ls by making their decision-making transparent and rationalized. Finally\,
I will discuss how to diagnose and improve models (e.g.\, robustness) thr
ough the lens of explanations. I will conclude with future research direct
ions that are centered around model interpretability and committed to faci
litating communications and interactions between intelligent machines\, sy
stem developers\, and end users for long-term trustworthy AI.
Hanjie Chen is a Ph.D. candidate in Compute r Science at the University of Virginia\, advised by Prof. Yangfeng Ji. He r research interests lie in Trustworthy AI\, Natural Language Processing ( NLP)\, and
\n X-TAGS;LANGUAGE=en-US:2023\,Chen\,February END:VEVENT BEGIN:VEVENT UID:ai1ec-24509@www.clsp.jhu.edu DTSTAMP:20240328T233544Z 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 Interpretable Machine Learning. She dev elops interpretation techniques to explain neural language models and make their prediction behavior transparent and reliable. She is a recipient of the Carlos and Esther Farrar Fellowship and the Best Poster Award at the ACM CAPWIC 2021. Her work has been published at top-tier NLP/AI conference s (e.g.\, ACL\, AAAI\, EMNLP\, NAACL) and selected by the National Center for Women & Information Technology (NCWIT) Collegiate Award Finalist 2021. She (as the primary instructor) co-designed and taught the course\, Inter pretable Machine Learning\, and was awarded the UVA CS Outstanding Graduat e Teaching Award and University-wide Graduate Teaching Awards Nominee (top 5% of graduate instructors). More details can be found at https://www.cs.virginia.edu/~hc9mx