Bio of Prof. Ming Li

Ming Li received his Ph.D. in Electrical Engineering from University of Southern California in 2013. He is currently an Associate Professor of Electrical and Computer Engineering at Division of Natural and Applied Science and Principal Research Scientist at Data Science Research Center at Duke Kunshan University. He is also an Adjunct Professor at School of Computer Science of Wuhan University. His research interests are in the areas of audio, speech and language processing as well as multimodal behavior signal analysis and interpretation. He has published more than 170 papers and served as the member of IEEE speech and language technical committee, CCF speech dialogue and auditory processing technical committee, CAAI affective intelligence technical committee, APSIPA speech and language processing technical committee. He was the area chair of speaker and language recognition at Interspeech 2016, Interspeech 2018, Interspeech 2020, SLT2022; the area chair of analysis of paralinguistics in speech and language at Interspeech 2024. He is the technical program co-chair at Odyssey 2022 and ASRU 2023. He is an editorial member of Computer Speech and Language and APSIPA Transactions on Signal and Information Processing. Works co-authored with his colleagues have won first prize awards at Interspeech Computational Paralinguistic Challenge 2011, 2012 and 2019, ASRU 2019 MGB-5 ADI challenge, Interspeech 2020 and 2021 fearless steps challenge, VoxSRC 2021, 2022 and 2023 challenge, ASVspoof21 challenge, ICASSP22 M2Met challenge, ICASSP23 MISP challenge and IJCAI ADD2023 challenge. He received the IBM faculty award in 2016, the ISCA Computer Speech and Language best journal paper award in 2018 and the youth achievement award of outstanding achievements of scientific research in higher education in 2020.


Welcome to the Speech and Multimodal Intelligent Information Processing (SMIIP) lab at Duke Kunshan University

Our research interests lie in the areas of intelligent speech processing as well as multimodal behavior signal analysis and interpretation.

  1. Intelligent speech processing: speaker verification, speaker diarization, paralinguistic state detection, anti-spoofing countermeasure, speech synthesis, voice conversion, keyword spotting, speech separation, spoken language identification, singing and music signal processing, etc.
  2. Multimodal behavior signal analysis and interpretation: Gathering, analyzing, modeling and interpreting multimodal human behavior signals (e.g. speech/language/audio/visual/physiological signal analysis and understanding) for assisted diagnose and treatment of autism spectrum disorders, etc.
  3. Pathological speech processing: laryngoscopic audio-visual signal processing, electronic laryngeal voice conversion