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We begin by reviewing how traditional speech processing has evolved with the advent of foundation models and audio-visual learning, and then delve into cutting-edge research that bridges audio, language, and visual modalities. The goal is to equip participants with both the conceptual frameworks and technical know-how to understand current audio-visual foundation models and to inspire new research directions at the nexus of speech and multimodality.\u003C/p>","2025-08-22T20:54:01.826Z","2025-08-22T20:55:39.033Z","2025-08-22T20:55:39.026Z","111",[1250,1259,1268],{"id":1251,"name":1252,"committee":13,"position":13,"affiliation":1253,"email":13,"biography":1254,"createdAt":1255,"updatedAt":1255,"url_path_id":1256,"contactPhoto":13,"socialLinks":1257,"url_path":1258},52,"David M Chan","UC Berkeley","\u003Cp style=\"text-align:justify;\">David M. Chan, Ph.D., is a postdoctoral scholar at the University of California, Berkeley, specializing in multimodal learning, human-computer interaction, and generative AI, including large language models (LLMs) and foundational models. In academia, his research focuses on developing efficient and scalable methods for integrating vision, audio, and language models, improving AI-human collaboration (i.e., Video Dubbing), and mitigating hallucination in generative AI systems. In industry, he has worked with leading organizations including Amazon, Google and NASA to develop scalable, safe, and accessible machine learning systems. David is also the maintainer of TSNE-CUDA, an open-source GPU-accelerated tool for high-dimensional data visualization used by over 40,000 researchers in fields ranging from biomedical technologies to manufacturing systems. He holds both a PhD. and MS in Computer Science from UC Berkeley and dual BSc degrees in Computer Science and Mathematics from the University of Denver.\u003C/p>","2025-08-22T20:49:09.750Z","108",[],"-63",{"id":1260,"name":1261,"committee":13,"position":13,"affiliation":1262,"email":13,"biography":1263,"createdAt":1264,"updatedAt":1264,"url_path_id":1265,"contactPhoto":13,"socialLinks":1266,"url_path":1267},53,"Huck Yang","NVIDIA Research","\u003Cp style=\"text-align:justify;\">Dr. Huck Yang is a Sr. Research Scientist at NVIDIA Research working on robust speech recognition and its connections to large-scale multimodal models. Prior to joining NVIDIA, he was a scientist at Amazon Alexa ASR-LM and a research intern at Google Brain. His research focuses on speech–language alignment, large-scale multilingual speech modeling, and adapting LLMs for speech processing tasks. Dr. Yang has developed the first cross-modal acoustic alignment techniques [ICML 2021] to integrate temporal signals into LLMs for time series, noise-robust ASR, audio captioning, and generative error correction. He also works on multilingual and privacy-preserving speech algorithms. He obtained his Ph.D. and M.Sc. from\u003Cbr>Georgia Institute of Technology, Atlanta, GA, under Prof. Chin-Hui Lee and B.Sc, from National Taiwan University. He has served in the Technical Committee in Applied Signal Processing Systems of IEEE SPS since 2022 and received a Best Student Paper Award Nomination at Interspeech 2023.\u003C/p>","2025-08-22T20:50:23.241Z","109",[],"-64",{"id":1269,"name":1270,"committee":13,"position":13,"affiliation":1271,"email":13,"biography":1272,"createdAt":1273,"updatedAt":1274,"url_path_id":1275,"contactPhoto":13,"socialLinks":1276,"url_path":1277},54,"Xudong Wang","Meta GenAI; incoming assistant Professor, Duke University, NC","\u003Cp style=\"text-align:justify;\">XuDong Wang is a Research Scientist in the GenAI group at Meta, working on video generation models. He received his Ph.D. in EECS from UC Berkeley, where he was affiliated with the Berkeley AI Research (BAIR) lab and was advised by Prof. Trevor Darrell. He was previously a researcher at Google DeepMind (GDM) and the International Computer Science Institute (ICSI), and a research intern at Meta’s Fundamental AI Research (FAIR) labs and Generative AI (GenAI) Research team. His research focuses on self-supervised learning, multimodal models, and machine learning, with an emphasis on developing foundational AI systems that go beyond the constraints of human supervision. By advancing self-supervised learning techniques for multimodal models—minimizing reliance on human-annotated data—he aims to build intelligent systems capable of understanding and interacting with their environment in ways that mirror, and potentially surpass, the complexity, adaptability, and richness of human intelligence. \u003C/p>","2025-08-22T20:51:38.114Z","2025-09-02T20:10:08.428Z","110",[],"-65","-66",{"id":415,"session":1280},{"id":415,"title":1281,"teaser":1282,"body":56,"createdAt":1283,"updatedAt":1284,"publishedAt":1285,"url_path_id":1286,"contacts":1287,"url_path":1320},"Scalable, personalized, and empathetic speech systems for everyone, everywhere","\u003Cp style=\"text-align:justify;\">Non-semantic speech metadata and paralinguistic signals carry information that are critical to modern voice AI. Such signals include the user's spoken language, accent, voice characteristics, emotion, and intent to activate or interact with the voice assistant. This tutorial offers a comprehensive overview on the most recent modelling approaches for non-semantic speech signals, from data acquisition to recent research advances empowered by foundation models. We share the advancements from both industry and academia, highlighting how diverse research tasks all contribute to the shared mission of creating scalable, personalized, and empathetic speech systems for everyone, everywhere.\u003C/p>","2025-08-22T21:02:00.168Z","2025-08-22T21:02:08.712Z","2025-08-22T21:02:08.707Z","117",[1288,1296,1303,1312],{"id":17,"name":1289,"committee":13,"position":13,"affiliation":1290,"email":13,"biography":1291,"createdAt":1292,"updatedAt":1292,"url_path_id":1293,"contactPhoto":13,"socialLinks":1294,"url_path":1295},"Min Ma","Google DeepMind","\u003Cp style=\"text-align:justify;\">Min received her Ph.D. degree from Graduate Center, The City University of New York. She is currently a researcher at Google DeepMind, working on multimodal foundation models (Gemini, Gemma), with a focus on speech, multilinguality and low-resource languages. Prior to that, she co-led the massive speech and text modeling program for thousands of languages. She has published over 20 peer-reviewed publications as a leading/core author, including a Best Paper at SLT 2022.\u003C/p>","2025-08-22T20:56:47.848Z","113",[],"-67",{"id":1170,"name":1297,"committee":13,"position":13,"affiliation":1290,"email":13,"biography":1298,"createdAt":1299,"updatedAt":1299,"url_path_id":1300,"contactPhoto":13,"socialLinks":1301,"url_path":1302},"Quan Wang","\u003Cp style=\"text-align:justify;\">Quan Wang received his B.Eng. degree from Tsinghua University in 2010. He received his Ph.D. degree from Rensselaer Polytechnic Institute with the Allen B. Dumont Prize in 2014. He is currently a Senior Staff Software Engineer at Google DeepMind, leading the Hotword Modeling team and the Speaker, Voice & Language team. He was a former Machine Learning Scientist at Amazon Alexa team, Cambridge from 2014 to 2015. He is a senior member of IEEE and member of ISCA. He is the author of the award winning Chinese textbook \"Voice Identity Techniques: From core algorithms to engineering practice\". He is also the instructor of the bestselling course \"Speaker Recognition\" on Udemy and Udemy Business.\u003C/p>","2025-08-22T20:57:40.842Z","114",[],"-68",{"id":1304,"name":1305,"committee":13,"position":13,"affiliation":1306,"email":13,"biography":1307,"createdAt":1308,"updatedAt":1308,"url_path_id":1309,"contactPhoto":13,"socialLinks":1310,"url_path":1311},57,"Sriram Ganapathy","Indian Institute of Science, Bangalore, India","\u003Cp style=\"text-align:justify;\">Sriram Ganapathy is an Associate Professor at the Electrical Engineering, Indian Institute of Science, Bangalore. Prior to that, he was a research staff member at the IBM Watson Research Center, Yorktown Heights. He received his PhD from the Center for Language and Speech Processing, Johns Hopkins University. He obtained his Bachelor of Technology from College of Engineering, Trivandrum, India and Master of Engineering from the Indian Institute of Science, Bangalore. He has also worked as a Visiting Research Scientist, at Google DeepMind, Bangalore. Over the past 15 years, he has authored 140+ peer-reviewed publications in the areas of deep learning, and speech/audio processing. Dr. Ganapathy served as the IEEE Sigport Chief Editor, member of the IEEE Education Board (2022-24), subject editor for Elsevier Speech Communication Journal and editorial board member of the Nature Scientific Data. He is a senior member of the IEEE Signal Processing Society and a member of ISCA.\u003C/p>","2025-08-22T20:59:09.998Z","115",[],"-69",{"id":1313,"name":1314,"committee":13,"position":13,"affiliation":1290,"email":13,"biography":1315,"createdAt":1316,"updatedAt":1316,"url_path_id":1317,"contactPhoto":13,"socialLinks":1318,"url_path":1319},58,"Daan van Esch","\u003Cp style=\"text-align:justify;\">Daan van Esch is a Senior Technical Program Manager with the Google DeepMind Speech team, working on internationalization for language technology at Google, harnessing machine learning and scalable infrastructure to bring support for new languages to products like Gboard, Translate, Search, YouTube, and Gemini. In his 12 years with Google, he's worked on text-to-speech synthesis, automatic speech recognition, keyboards, machine translation, and more. Daan holds a degree in Chinese Studies from Leiden University, the Netherlands, where his research focus was Chinese linguistics and natural-language processing.\u003C/p>","2025-08-22T21:00:07.262Z","116",[],"-70","-71",{"id":135,"session":1322},{"id":135,"title":1323,"teaser":1324,"body":56,"createdAt":1325,"updatedAt":1326,"publishedAt":1327,"url_path_id":1328,"contacts":1329,"url_path":1348},"Bridging the domain and modality gaps of speech based on optimal transport (OT)","\u003Cp style=\"text-align:justify;\">This tutorial introduces optimal transport (OT) as a powerful mathematical framework for comparing and transforming probability distributions, with a focus on speech processing applications. OT offers a geometry-aware distance metric that is particularly useful in tasks like feature learning, domain adaptation, and multi-modal knowledge transfer. Participants will learn both foundational concepts and advanced techniques for integrating OT into deep learning-based speech processing pipelines. The session will also highlight recent research findings and practical insights to inspire further exploration in this emerging area.\u003C/p>","2025-08-22T21:07:56.563Z","2025-08-22T21:08:00.952Z","2025-08-22T21:08:00.942Z","120",[1330,1339],{"id":1331,"name":1332,"committee":13,"position":13,"affiliation":1333,"email":13,"biography":1334,"createdAt":1335,"updatedAt":1335,"url_path_id":1336,"contactPhoto":13,"socialLinks":1337,"url_path":1338},59,"Xugang Lu","Advanced Speech Technology Laboratory, National Institute of Information and Communications Technology (NICT), Japan","\u003Cp style=\"text-align:justify;\">Dr. Xugang Lu (Senior member, IEEE) received his B.S. and M.S. degrees from Harbin Institute of Technology, China, in 1994 and 1996, respectively. He completed his Ph.D. in Chinese Academy of Sciences, in 1999. From October 1999 to December 2002, he worked as a research fellow at Nanyang Technological University, Singapore, and a postdoctoral fellow at McMaster University, Canada. Between April 2003 and April 2008, he was an assistant professor at Japan Advanced Institute of Science and Technology. After May 2008, he joined the National Institute of Information and Communications Technology, Japan, as a senior researcher. From 2017 to 2023, he held a guest professor position at Doshisha University, Japan. He serves as an Associate Editor for the IEEE/ACM Transactions on Audio, Speech, and Language Processing. His primary research interests include speech communications and statistical machine learning.\u003C/p>","2025-08-22T21:04:20.642Z","118",[],"-72",{"id":1340,"name":1341,"committee":13,"position":13,"affiliation":1342,"email":13,"biography":1343,"createdAt":1344,"updatedAt":1344,"url_path_id":1345,"contactPhoto":13,"socialLinks":1346,"url_path":1347},60,"Yu Tsao","Research Center for Information Technology Innovation (CITI), Academia Sinica, Taiwan","\u003Cp style=\"text-align:justify;\">Dr. Yu Tsao (Senior Member, IEEE) obtained his B.S. and M.S. degrees from National Taiwan University, in 1999 and 2001, respectively. He earned his Ph.D. degree from the Georgia Institute of Technology, USA, in 2008. From 2009 to 2011, he worked at the National Institute of Information and Communications Technology, Japan. Currently, he holds the position of professor and deputy director at the Research Center for Information Technology Innovation, Academia Sinica, Taiwan. Additionally, he is a jointly appointed professor at Chung Yuan Christian University, Taiwan. He serves as an Associate Editor for the IEEE/ACM Transactions on Audio, Speech, and Language Processing and IEEE Signal Processing Letters. Dr. Tsao's research interests encompass assistive oral communication technologies, audio coding, and bio-signal processing.\u003C/p>","2025-08-22T21:05:53.456Z","119",[],"-73","-74",{"id":90,"session":1350},{"id":90,"title":1351,"teaser":1352,"body":56,"createdAt":1353,"updatedAt":1354,"publishedAt":1355,"url_path_id":1356,"contacts":1357,"url_path":1400},"Massive Sound Embedding Benchmark (MSEB): A Unified Framework for Evaluating General-Purpose Audio Representations","\u003Cp style=\"text-align:justify;\">Audio is a critical component of multimodal perception, requiring systems to demonstrate a wide range of capabilities, from transcription and classification to retrieval, reasoning, and segmentation. Fundamentally, these tasks rely on transforming raw audio into meaningful embeddings. We present the Massive Sound Embedding Benchmark (MSEB): an extensible, open-source framework designed to comprehensively evaluate the core auditory components of any multimodal system. MSEB's first release offers a comprehensive suite of eight tasks, supported by diverse datasets, including the new, large-scale Simple Voice Questions (SVQ) dataset. This tutorial will introduce the framework, review initial performance headrooms, and encourage community contributions to accelerate progress toward robust machine auditory intelligence. \u003C/p>","2025-08-22T21:14:46.960Z","2025-11-12T02:41:25.488Z","2025-08-22T21:14:49.113Z","125",[1358,1367,1376,1383,1391],{"id":1359,"name":1360,"committee":13,"position":13,"affiliation":1361,"email":13,"biography":1362,"createdAt":1363,"updatedAt":1363,"url_path_id":1364,"contactPhoto":13,"socialLinks":1365,"url_path":1366},61,"Georg Heigold","Staff Research Scientist, Google","\u003Cp style=\"text-align:justify;\">Georg Heigold is a Research Scientist at Google, specializing in large-scale machine learning models for speech and audio processing. His research work includes efficient learning algorithms and model architectures, with a focus on creating scalable representations for multimodal understanding. He is dedicated to building foundational models that exhibit strong generalization capabilities across diverse downstream tasks.\u003C/p>","2025-08-22T21:09:55.853Z","121",[],"-75",{"id":1368,"name":1369,"committee":13,"position":13,"affiliation":1370,"email":13,"biography":1371,"createdAt":1372,"updatedAt":1372,"url_path_id":1373,"contactPhoto":13,"socialLinks":1374,"url_path":1375},62,"Tom Bagby","Staff Software Engineer, Google","\u003Cp style=\"text-align:justify;\">Tom Bagby is a Staff Software Engineer at Google with extensive experience in audio signal processing and machine learning. His work centers on developing novel algorithms for sound event detection, acoustic scene analysis, and audio-based retrieval systems. He has contributed to several key projects that leverage audio embeddings for real-world applications.\u003C/p>","2025-08-22T21:10:56.323Z","122",[],"-76",{"id":37,"name":1377,"committee":13,"position":13,"affiliation":1378,"email":13,"biography":56,"createdAt":1379,"updatedAt":1379,"url_path_id":1380,"contactPhoto":13,"socialLinks":1381,"url_path":1382},"Cyril Allauzen","Senior Staff Research Scientist, Google","2025-11-12T02:40:08.959Z","153",[],"-98",{"id":1384,"name":1385,"committee":13,"position":13,"affiliation":1378,"email":13,"biography":1386,"createdAt":1387,"updatedAt":1387,"url_path_id":1388,"contactPhoto":13,"socialLinks":1389,"url_path":1390},63,"Ehsan Variani","\u003Cp style=\"text-align:justify;\">Ehsan Variani is a Senior Staff Research Scientist at Google and a key contributor to the development of many speech models. His research has advanced the state-of-the-art in speech recognition, speaker verification, and representation learning. He focuses on creating unified models that simplify complex audio understanding pipelines.\u003C/p>","2025-08-22T21:12:13.639Z","123",[],"-77",{"id":1392,"name":1393,"committee":13,"position":13,"affiliation":1394,"email":13,"biography":1395,"createdAt":1396,"updatedAt":1396,"url_path_id":1397,"contactPhoto":13,"socialLinks":1398,"url_path":1399},64,"Michael Riley","Distinguished Scientist, Google","\u003Cp style=\"text-align:justify;\">Michael Riley is a Distinguished Scientist at Google and a Fellow of the IEEE and ISCA. He is a world-renowned expert in speech processing and weighted finite-state transducers (WFSTs), having co-developed the OpenFst library. His foundational work has been instrumental in the success of modern ASR and TTS systems, and he is currently exploring next-generation modeling paradigms.\u003C/p>","2025-08-22T21:13:20.582Z","124",[],"-78","-79",{"data":1402,"meta":1403},{"id":590,"heading":591,"createdAt":596,"updatedAt":597,"publishedAt":598,"url_path_id":599,"url_path":592,"contentType":95},{},1778851853257]