Dr. Shinji Watanabe, Carnegie Mellon University をお招きして、セミナーを開催いたします。
参加希望の方は、下記Googleフォームにて申し込みください。申込は東北大学学内限定です。(tohoku.ac.jpの学内アドレスよりお申し込みください)
We are pleased to invite Dr.Shinji Watanabe(Carnegie Mellon University) to join us for a seminar.If you would like to attend, please register using the Google Form below.
| 開催日時/Date | 2025年7月14日 (火) 11:00～12:00 |
| 講演題目/Title | Reproducing Large Speech Foundation Models for Open Science |
| 現地場所/Location | 東北大学 川内キャンパス マルチメディア教育研究棟(A05) 2F M203講義室 【マルチメディア教育研究棟】 https://www.tohoku.ac.jp/japanese/profile/campus/01/kawauchi/areaa.html 地図中のA05の建物．▲が建物入り口 【マルチメディアホール各階案内】 https://www2.he.tohoku.ac.jp/center/mm_intro/mm_intro.html |
| 対象者/Target Audience | 学内の研究者、学生、関係者（東北大学学内限定） Researchers, students, and related persons on campus (Tohoku University campus only) |
| 備考/Note | ※当日現地での参加登録も可能です。 *You can also register on-site on the day of the event. |
Title:
Reproducing Large Speech Foundation Models for Open Science
Speaker:
Dr. Shinji Watanabe (Carnegie Mellon University)
Abstract:
Speech foundation models are transforming the field by unifying diverse speech-processing tasks through large-scale data, increased model capacity, and task diversity. This paradigm shift has led to a growing division of research roles: large technology companies primarily develop foundational models, while academic and smaller research groups focus on adaptation, analysis, and downstream applications. This separation raises concerns about transparency, reproducibility, and explainability. To address these challenges, our group at Carnegie Mellon University has developed Open Whisper-style Speech Models (OWSM), reproducing OpenAI Whisper-style training using only publicly available data and the open-source toolkit ESPnet as an effort toward open science. Because the entire training pipeline is transparent, OWSM enables reproducible research and more explainable model behaviors. In addition, we will present our recent efforts on pre-training large-scale multimodal speech–text language models and discuss the research challenges they raise. This presentation highlights both the technical advances and the open challenges in reproducing large speech foundation models, emphasizing the role of openness and transparency in advancing accessible, interpretable speech and audio technologies.
Bio:
Shinji Watanabe is an Associate Professor at Carnegie Mellon University, Pittsburgh, PA. He received his B.S., M.S., and Ph.D. (Dr. Eng.) degrees from Waseda University, Tokyo, Japan. He was a research scientist at NTT Communication Science Laboratories, Kyoto, Japan, from 2001 to 2011, a visiting scholar at Georgia Institute of Technology, Atlanta, GA, in 2009, and a senior principal research scientist at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA, from 2012 to 2017. Before Carnegie Mellon University, he was an associate research professor at Johns Hopkins University, Baltimore, MD, USA, from 2017 to 2020. His research interests include automatic speech recognition, speech enhancement, spoken language understanding, and machine learning for speech and language processing. He has published over 600 papers in peer-reviewed journals and conferences and received several awards, including the best paper award from ISCA Interspeech in 2024. He has been a member of several technical committees, including the IEEE Signal Processing Society Speech and Language Technical Committee (SLTC, Chair). He is an IEEE and ISCA Fellow.
