In this episode of the IEEE Signal Processing Society podcast, Dr. Sanjeev Khudanpur, Director of the Center for Language and Speech Processing, Johns Hopkins University interviews Associate Prof. Shinji Watanabe, Language Technologies Institute, Carnegie Mellon University. They talk about the latest research and innovations in speech recognition technologies and their impact across various industries.

Shinji Watanabe

Shinji Watanabe is an Associate Professor at Carnegie Mellon University in Pittsburgh and a leading researcher in speech and language processing. His work spans automatic speech recognition, speech enhancement, spoken language understanding, and machine learning for speech and language processing. He has contributed more than 500 publications to peer-reviewed journals and received several awards, including the best paper award from ISCA Interspeech 2024.

In this episode, Associate Prof. Watanabe reflects on the transformative progress in speech recognition over the past decade, highlighting milestones from the adoption of deep neural networks to the rise of large-scale models like OpenAI Whisper. He discusses the ongoing challenges in achieving human-level understanding in complex scenarios such as multi-speaker conversations, accented and multilingual speech, and child or disordered speech. He concludes with thoughts on academia’s enduring role in shaping the field, and how his inspiration is often drawn from science fiction and Japanese animation.

In this episode of the IEEE Signal Processing Society podcast, Prof. Samson Lasaulce, Chief Research Scientist at Khalifa University (KU) interviews Prof. Merouane Debbah, founding Director of the KU 6G Research Center. They talk about how AI is transforming the future of wireless communications, the use of large language models (LLMs) to revolutionize network management, improve communication protocols, and advance automation.

About the speaker
Mérouane Debbah is a Professor at Khalifa University of Science and Technology in Abu Dhabi and founding Director of the KU 6G Research Center. His research bridges mathematics, algorithms, statistics, and communication sciences, with a focus on random matrix theory and learning algorithms. He has played a pivotal role in advancing small cells (4G), Massive MIMO (5G), and Large Intelligent Surfaces (6G) technologies. He is a frequent keynote speaker at international events in the field of telecommunication and AI.

In this episode, Professor Debbah explores the transformative potential of LLMs in wireless communication. Key applications include dynamic spectrum management, end-to-end system optimization, fault detection, mobility management, and AI-based localization. These techniques enhance data handling and system performance by replacing complex modeling with efficient data-driven approaches. These insights shed light on how 6G, powered by pervasive AI, will revolutionize wireless networks and redefine communication frameworks for the future.

In this episode of the IEEE Signal Processing Society podcast, Dennis K. Chrogony, Education Board Outreach and Visibility Committee Member interviews Sergios Theodoridis, Professor Emeritus, Signal Processing and Machine Learning, National and Kapodistrian University of Athens, Greece, Aalborg University, Denmark, and Shenzhen Research Institute of Big Data, Chinese University of Hong Kong, China. They delve into the evolution and impact of signal processing and AI and machine learning on technological advancements.

Professor Sergios Theodoridis
Prof. Theodoridis has made countless contributions to the field of signal processing and machine learning. He has authored several books, received multiple awards, and earned prestigious accolades like the EURASIP Athanasios Papoulis Award and the IEEE Signal Processing Society Education Award. His leadership positions, including his role as Vice President of the IEEE Signal Processing Society highlight his significant influence and respect within the academic and professional community.

In this episode, Prof. Theodoridis discusses the advancements in various areas of signal processing, highlighting AI and machine learning as pivotal technologies in modern society. He explains how the seamless integration of machine learning into signal processing has led to significant improvements in areas like speech and audio recognition and natural language processing.

He also notes that while the core goals of signal processing remain unchanged, new techniques from the machine learning community have greatly enhanced its applications.

In the first podcast sponsored by the IEEE Signal Processing Society, Ervin Sejdić, Professor at University of Toronto’s Edward S. Rogers Sr. Department of Electrical & Computer Engineering interviews April Khademi, Associate Professor of Biomedical Computer and Electrical Engineering at Toronto Metropolitan University and Canada Research Chair in AI for Medical Imaging.

April Khademi

April, an expert in AI-driven medical image analysis, focuses on AI-driven signal processing methods that have revolutionized medical imaging, improving diagnostic and therapeutic accuracy. In this podcast, she discusses how AI advancements continuously push the boundaries of medical imaging, providing clinicians with robust, quantitative disease metrics and enhancing overall healthcare quality.

AI-driven foundation models in medical imaging, like MRI and CT, enhance object detection and segmentation. They mitigate data constraints and enable fine-tuning for smaller datasets. FDA-approved algorithms in image acquisition improve efficiency, allowing faster, lower-dose scans and super-resolution for better image quality, yielding substantial business benefits. AI also enhances interrater agreement in medical image interpretation, reducing subjectivity and varying expertise among clinicians. It ensures consistent, accurate diagnoses, especially in community hospitals without specialized pathologists, ultimately leading to more reliable patient treatment outcomes.