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:20240329T051339Z 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-23304@www.clsp.jhu.edu DTSTAMP:20240329T051339Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nTransformers are essential to pretraining. As we approach 5 years of BERT\, the connection between a ttention as architecture and transfer learning remains key to this central thread in NLP. Other architectures such as CNNs and RNNs have been used t o replicate pretraining results\, but these either fail to reach the same accuracy or require supplemental attention layers. This work revisits the semanal BERT result and considers pretraining without attention. We consid er replacing self-attention layers with recently developed approach for lo ng-range sequence modeling and transformer architecture variants. Specific ally\, inspired by recent papers like the structured space space sequence model (S4)\, we use simple routing layers based on state-space models (SSM ) and a bidirectional model architecture based on multiplicative gating. W e discuss the results of the proposed Bidirectional Gated SSM (BiGS) and p resent a range of analysis into its properties. Results show that architec ture does seem to have a notable impact on downstream performance and a di fferent inductive bias that is worth exploring further.
\nBi ography
\n