Speech Synthesis and Voice Conversion: Machine Learning can Mimic Anyone's Voice - Berrak Sisman скачать в хорошем качестве

Speech Synthesis and Voice Conversion: Machine Learning can Mimic Anyone's Voice - Berrak Sisman

Speaker: Berrak Sisman, University of Texas at Dallas Abstract: Voice 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 linguistic 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 conversion involves multiple speech processing techniques, such as speech analysis, spectral conversion, prosody conversion, speaker characterization, and vocoding. With the recent advances in theory and practice, we are now able to produce human-like voice quality with high speaker similarity. In this talk, Dr. Sisman will present the recent advances in voice conversion and discuss their promise and limitations. Dr. Sisman will also provide a summary of the available resources for expressive voice conversion research. Bio: Dr. Berrak Sisman (Member, IEEE) received the Ph.D. degree in electrical and computer engineering from National University of Singapore in 2020, fully funded by A*STAR Graduate Academy under Singapore International Graduate Award (SINGA). She is currently working as 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 Singapore University of Technology and Design (2020-2022). She was a Postdoctoral 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), University of Edinburgh (2019). She was a visiting researcher at RIKEN Advanced Intelligence Project in Japan (2018). Her research is focused on machine learning, signal processing, emotion, speech synthesis and voice conversion. Dr. Sisman has served 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 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 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 Association (ISCA).