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Base","Card Contact Full",[1235],{"id":48,"groupTitle":13,"sessions":1236},[1237,1289,1342,1380],{"id":227,"session":1238},{"id":227,"title":1239,"teaser":1240,"body":1241,"createdAt":1242,"updatedAt":1243,"publishedAt":1244,"url_path_id":1245,"contacts":1246,"url_path":1288},"Towards Unsupervised Training for Seq-to-Seq Processing","\u003Cp style=\"text-align:justify;\">The success of mainstream machine learning for ASR (automatic speech recognition) and similar tasks is largely based on supervised training, which requires pairs of [audio,text] strings as training data.  In contrast, the assumption for unsupervised training is that no explicit pairs of [audio,text] strings are available.  On a task like Librispeech, this would mean that we are given the audio strings only (without any transcriptions) as training data, along with the domain-specific text data for training the language model.\u003C/p>","\u003Cp style=\"text-align:justify;\">Early work towards general unsupervised training started on seq-to-seq processing tasks like decipherment of encrypted texts (\"substitution ciphers\", Peleg et al.~1979, Lee 2002,  Knight et al. 2006) and optical character recognition (\"cryptogram solving\", Casey 1986). It was only recently that unsupervised training was studied for large-vocabulary  ASR tasks (like Librispeech; Baevski et al. 2021) that require a fully fledged recognition system.\u003C/p>\u003Cp style=\"text-align:justify;\">This keynote talk will address the state of the art in terms of practical work and theoretical considerations. When developing a conceptual framework for unsupervised training, we are faced with a couple of fundamental questions:  Can unsupervised training work at all? What are the necessary conditions? What are suitable training criteria? Under what conditions can we expect good results? How close can we get to supervised training?\u003C/p>","2025-05-22T18:09:38.805Z","2025-05-22T18:11:28.754Z","2025-05-22T18:09:40.143Z","94",[1247],{"id":1222,"name":1248,"committee":13,"position":13,"affiliation":1249,"email":13,"biography":1250,"createdAt":1251,"updatedAt":1251,"url_path_id":1252,"contactPhoto":1253,"socialLinks":1286,"url_path":1287},"Hermann Ney","AppTek, McLean, VA | RWTH Aachen University, Germany","\u003Cp style=\"text-align:justify;\">Hermann Ney is director of science at AppTek, McLean, VA and  senior professor of computer science at RWTH Aachen University, Germany. His main research interests lie in the area of machine learning,  neural networks and  applications to speech recognition, machine translation and other tasks in natural language processing.  \u003C/p>\u003Cp style=\"text-align:justify;\">His team contributed to a large number of European (e.g. TC-STAR, QUAERO,  TRANSLECTURES, EU-BRIDGE) and US-American (e.g. GALE, BOLT, BABEL) large-scale joint projects. His work has resulted in more than 700 conference and journal papers with an h index of 117 and 69000 citations (based on Google scholar). More than 50 of his former PhD students work for IT companies like Amazon, Apple, Cerence, Ebay, Google and Nuance/Microsoft.\u003C/p>\u003Cp style=\"text-align:justify;\">The results of his research contributed to various operational research prototypes and commercial systems. In 1993 Philips Dictation Systems Vienna introduced a large-vocabulary continuous-speech recognition product for medical text dictation. In 1997 Philips Dialogue Systems Aachen introduced a spoken dialogue system for traintable information via the telephone.  In the German project VERBMOBIL, his team introduced the phrase-based approach  to data-driven machine translation, which in 2008 was used by his former  PhD students at Google as starting point for the service Google Translate.  In the EU project TC-STAR, the first research prototype system  for spoken language translation of real-life domains was built.\u003C/p>\u003Cp style=\"text-align:justify;\">Awards: \u003Cbr>2005 Technical Achievement Award of the IEEE Signal Processing Society; \u003Cbr>2013 Award of Honour of the International Association for Machine Translation; \u003Cbr>2019 IEEE James L. Flanagan Speech and Audio Processing Award; \u003Cbr>2021 ISCA Medal for Scientific Achievements (ISCA: Int. Speech Communication Ass.). \u003C/p>","2025-05-22T18:08:27.526Z","93",{"id":636,"name":1254,"alternativeText":13,"caption":13,"width":1255,"height":1256,"formats":1257,"hash":1282,"ext":956,"mime":959,"size":1283,"url":1284,"previewUrl":13,"provider":41,"provider_metadata":13,"createdAt":1285,"updatedAt":1285},"Photo_IEEE_ASRU2025_20May25.jpg",3908,2602,{"large":1258,"small":1264,"medium":1270,"thumbnail":1276},{"ext":956,"url":1259,"hash":1260,"mime":959,"name":1261,"path":13,"size":1262,"width":779,"height":1263},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/large_Photo_IEEE_ASRU_2025_20_May25_6b1b855b16.jpg","large_Photo_IEEE_ASRU_2025_20_May25_6b1b855b16","large_Photo_IEEE_ASRU2025_20May25.jpg",51.76,666,{"ext":956,"url":1265,"hash":1266,"mime":959,"name":1267,"path":13,"size":1268,"width":29,"height":1269},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/small_Photo_IEEE_ASRU_2025_20_May25_6b1b855b16.jpg","small_Photo_IEEE_ASRU_2025_20_May25_6b1b855b16","small_Photo_IEEE_ASRU2025_20May25.jpg",15.16,333,{"ext":956,"url":1271,"hash":1272,"mime":959,"name":1273,"path":13,"size":1274,"width":791,"height":1275},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/medium_Photo_IEEE_ASRU_2025_20_May25_6b1b855b16.jpg","medium_Photo_IEEE_ASRU_2025_20_May25_6b1b855b16","medium_Photo_IEEE_ASRU2025_20May25.jpg",30.39,499,{"ext":956,"url":1277,"hash":1278,"mime":959,"name":1279,"path":13,"size":1280,"width":1281,"height":972},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/thumbnail_Photo_IEEE_ASRU_2025_20_May25_6b1b855b16.jpg","thumbnail_Photo_IEEE_ASRU_2025_20_May25_6b1b855b16","thumbnail_Photo_IEEE_ASRU2025_20May25.jpg",4.66,234,"Photo_IEEE_ASRU_2025_20_May25_6b1b855b16",581,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/Photo_IEEE_ASRU_2025_20_May25_6b1b855b16.jpg","2025-05-22T18:07:20.763Z",[],"-53","-54",{"id":359,"session":1290},{"id":359,"title":1291,"teaser":1292,"body":56,"createdAt":1293,"updatedAt":1294,"publishedAt":1295,"url_path_id":1296,"contacts":1297,"url_path":1341},"On the (Co-)Evolution of Universal Written, Spoken, and Signed Language Processing","\u003Cp style=\"text-align:justify;\">Natural language processing research has evolved over the past few years from mainly task-specific models, then to task-independent representation models fine-tuned for various tasks, and finally to fully task-independent language models.  This progression addresses a desire for universality in the sense of handling arbitrary tasks in the same model.  Another dimension of universality is the ability to serve arbitrary types of language users, regardless of their choice of language, dialect, or other individual properties.  Progress toward universality has historically been addressed largely independently in separate research communities focusing on written, spoken, and signed language, although they share many similarities.  This talk will trace the recent progress toward universality in these three language modalities, while highlighting a few pieces of recent work.\u003C/p>\u003Cp>\u003Cbr> \u003C/p>","2025-06-13T20:43:05.656Z","2025-08-01T14:13:13.389Z","2025-06-13T20:48:27.430Z","99",[1298],{"id":1299,"name":1300,"committee":13,"position":13,"affiliation":1301,"email":13,"biography":1302,"createdAt":1303,"updatedAt":1304,"url_path_id":1305,"contactPhoto":1306,"socialLinks":1339,"url_path":1340},50,"Karen Livescu","TTI-Chicago","\u003Cp style=\"text-align:justify;\">Karen Livescu is a Professor at TTI-Chicago, where she has been since 2008.  She also has a courtesy faculty appointment at the University of Chicago computer science department and is affiliated with its Data Science Institute.  She completed her PhD and postdoc at MIT in the EECS department, after a Bachelor's degree in physics at Princeton University.  Her PhD research was on speech recognition and pronunciation modeling with articulatory features.  Her research group has since expanded to work on a broad variety of topics in spoken, written, and signed language processing, with a particular interest in representation learning, cross-modality learning, and low-resource settings.  She is a Fellow of the IEEE and ISCA.  She has served as a program chair/co-chair for Interspeech, ASRU, and ICLR and as an Associate Editor for TACL, IEEE T-PAMI, IEEE T-ASLP, IEEE OJ-SP, and others.  \u003Cbr> \u003C/p>","2025-06-13T20:42:18.398Z","2025-06-13T20:47:36.597Z","98",{"id":476,"name":1307,"alternativeText":13,"caption":13,"width":1308,"height":1309,"formats":1310,"hash":1335,"ext":1312,"mime":959,"size":1336,"url":1337,"previewUrl":13,"provider":41,"provider_metadata":13,"createdAt":1338,"updatedAt":1338},"Livescu_head_shot_high_res.JPG",3128,3280,{"large":1311,"small":1318,"medium":1324,"thumbnail":1330},{"ext":1312,"url":1313,"hash":1314,"mime":959,"name":1315,"path":13,"size":1316,"width":1317,"height":779},".JPG","https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/large_Livescu_head_shot_high_res_9bd458dac3.JPG","large_Livescu_head_shot_high_res_9bd458dac3","large_Livescu_head_shot_high_res.JPG",98.38,954,{"ext":1312,"url":1319,"hash":1320,"mime":959,"name":1321,"path":13,"size":1322,"width":1323,"height":29},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/small_Livescu_head_shot_high_res_9bd458dac3.JPG","small_Livescu_head_shot_high_res_9bd458dac3","small_Livescu_head_shot_high_res.JPG",28.28,477,{"ext":1312,"url":1325,"hash":1326,"mime":959,"name":1327,"path":13,"size":1328,"width":1329,"height":791},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/medium_Livescu_head_shot_high_res_9bd458dac3.JPG","medium_Livescu_head_shot_high_res_9bd458dac3","medium_Livescu_head_shot_high_res.JPG",58.03,715,{"ext":1312,"url":1331,"hash":1332,"mime":959,"name":1333,"path":13,"size":1334,"width":476,"height":972},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/thumbnail_Livescu_head_shot_high_res_9bd458dac3.JPG","thumbnail_Livescu_head_shot_high_res_9bd458dac3","thumbnail_Livescu_head_shot_high_res.JPG",4.41,"Livescu_head_shot_high_res_9bd458dac3",779.96,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/Livescu_head_shot_high_res_9bd458dac3.JPG","2025-06-13T20:41:50.455Z",[],"-56","-57",{"id":207,"session":1343},{"id":207,"title":1344,"teaser":1345,"body":56,"createdAt":1346,"updatedAt":1347,"publishedAt":1348,"url_path_id":1349,"contacts":1350,"url_path":1379},"For Speech Recognition to Document Understanding. A Journey for a Speech Scientist","\u003Cp style=\"text-align:justify;\">In this talk, I will describe my journey as an old-style speech scientist from classic technologies (HMMs, concatenative TTS, GMMs) to modern speech recognition and then to image and document processing. In the talk I'll focus on how modern LLM technologies have removed barriers across what used to be disjoint disciplines (speech, vision, NLP) and how nowadays even profesional barriers are to a large degree irrelevant. Scientists with zero experience in speech can bring amazing contributions to the field. Undergraduates can build amazing TTS engines in a matter of months with no previous experience. I'll describe my journey building document understanding technologies and share some observations along this personal trip.\u003C/p>","2025-05-14T17:28:03.979Z","2025-05-14T17:28:09.927Z","2025-05-14T17:28:09.922Z","91",[1351],{"id":1352,"name":1353,"committee":13,"position":13,"affiliation":1354,"email":13,"biography":1355,"createdAt":1356,"updatedAt":1357,"url_path_id":1358,"contactPhoto":1359,"socialLinks":1377,"url_path":1378},48,"Pedro J. Moreno","SDAIA, SA","\u003Cp style=\"text-align:justify;\">Pedro J. Moreno has a lengthy history in the field of speech science, spanning over three decades. After completing his Master's degree in Telecommunications Engineering from Universidad Politecnica de Madrid, he spent 3 years  at Bell Laboratories as a visiting scientist. Subsequently, he was awarded a Fulbright Scholarship, which allowed him to pursue his doctoral studies at Carnegie Mellon University.\u003C/p>\u003Cp style=\"text-align:justify;\">Upon graduating, he spent some time at HP Labs where he led projects in core machine learning and audio search. In 2004 he joined Google Research. At Google, he held a leadership position as the head of the ASR R&D team until April 2024. During his tenure there, he worked on various innovative projects, such as the development of google search by voice, youtube video captioning, speech-to-speech voice conversion, speech recognition internationalization and the development of neural based multilingual speech recognition systems, as well as contextual modeling in ASR.\u003C/p>\u003Cp style=\"text-align:justify;\">In August 2024, he took on a new role as Chief Scientist at the National Center of AI at SDAIA in Saudi Arabia where he led projects in speech, vision, and document understanding. In March of 2025 he was recognized as an IEEE Fellow for his contributions to speech noise robustness and industrial speech recognition. Starting April 2025 together with his team at SDAIA he transitioned to Humain.ai a novel research and development company where he has the tole of Chief AI Researcher leading projects in multimedia.\u003C/p>","2025-05-14T17:25:45.054Z","2025-07-25T21:45:38.448Z","90",{"id":30,"name":1360,"alternativeText":13,"caption":13,"width":1361,"height":1361,"formats":1362,"hash":1373,"ext":23,"mime":26,"size":1374,"url":1375,"previewUrl":13,"provider":41,"provider_metadata":13,"createdAt":1376,"updatedAt":1376},"Pedro J. Moreno.png",546,{"small":1363,"thumbnail":1368},{"ext":23,"url":1364,"hash":1365,"mime":26,"name":1366,"path":13,"size":1367,"width":29,"height":29},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/small_Pedro_J_Moreno_4818df51ea.png","small_Pedro_J_Moreno_4818df51ea","small_Pedro J. Moreno.png",310.77,{"ext":23,"url":1369,"hash":1370,"mime":26,"name":1371,"path":13,"size":1372,"width":972,"height":972},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/thumbnail_Pedro_J_Moreno_4818df51ea.png","thumbnail_Pedro_J_Moreno_4818df51ea","thumbnail_Pedro J. Moreno.png",37.66,"Pedro_J_Moreno_4818df51ea",88.25,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/Pedro_J_Moreno_4818df51ea.png","2025-07-25T21:45:05.837Z",[],"-51","-52",{"id":59,"session":1381},{"id":59,"title":1382,"teaser":1383,"body":56,"createdAt":1384,"updatedAt":1385,"publishedAt":1386,"url_path_id":1387,"contacts":1388,"url_path":1426},"Hallucination in Automatic Speech Recognition Systems","\u003Cp style=\"text-align:justify;\">Large language models are famously prone to \"hallucinations\"—generating fictitious responses not grounded in reality, often unrelated to the input or query—necessitating mitigation strategies such as alignment and postfiltering. Modern automatic speech recognition (ASR) systems, their less famous siblings, are also prone to hallucinations, generating responses that are unrelated to the input, sometimes in the absence of any stimulus. Unlike LLMs, however, the definition of hallucination becomes more challenging, since they are indistinguishable from regular insertion and substitution errors. This talk will examine the problem of hallucination in ASR systems, how they may be defined, how they may be quantified, and what mitigation strategies may be employed.\u003Cbr> \u003C/p>","2025-07-16T15:05:42.692Z","2025-08-04T13:57:40.524Z","2025-07-16T15:05:45.322Z","101",[1389],{"id":1390,"name":1391,"committee":13,"position":13,"affiliation":1392,"email":13,"biography":1393,"createdAt":1394,"updatedAt":1395,"url_path_id":1396,"contactPhoto":1397,"socialLinks":1424,"url_path":1425},51,"Bhiksha Raj","Carnegie Mellon University","\u003Cp style=\"text-align:justify;\">Bhiksha Raj is a professor in the Language Technologies Institute, of the School of Computer Science at Carnegie Mellon University, with additional affiliations to the Machine Learning and Electrical and Computer Engineering departments. Raj obtained his PhD from CMU in 2000 and was at Mistubishi Electric Research Laboratories from 2001-2008. Dr. Raj's chief research interests lie in automatic speech recognition, computer audition, machine learning and data privacy. He is a Fellow of ISCA and a Fellow of the IEEE.\u003C/p>","2025-07-16T15:04:10.252Z","2025-07-25T21:51:39.381Z","100",{"id":261,"name":1398,"alternativeText":13,"caption":13,"width":1399,"height":1400,"formats":1401,"hash":1420,"ext":956,"mime":959,"size":1421,"url":1422,"previewUrl":13,"provider":41,"provider_metadata":13,"createdAt":1423,"updatedAt":1423},"Bhiksha Raj.jpg",862,872,{"small":1402,"medium":1408,"thumbnail":1414},{"ext":956,"url":1403,"hash":1404,"mime":959,"name":1405,"path":13,"size":1406,"width":1407,"height":29},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/small_Bhiksha_Raj_deb68df204.jpg","small_Bhiksha_Raj_deb68df204","small_Bhiksha Raj.jpg",30.57,494,{"ext":956,"url":1409,"hash":1410,"mime":959,"name":1411,"path":13,"size":1412,"width":1413,"height":791},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/medium_Bhiksha_Raj_deb68df204.jpg","medium_Bhiksha_Raj_deb68df204","medium_Bhiksha Raj.jpg",60.67,741,{"ext":956,"url":1415,"hash":1416,"mime":959,"name":1417,"path":13,"size":1418,"width":1419,"height":972},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/thumbnail_Bhiksha_Raj_deb68df204.jpg","thumbnail_Bhiksha_Raj_deb68df204","thumbnail_Bhiksha Raj.jpg",5.23,154,"Bhiksha_Raj_deb68df204",78.57,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/Bhiksha_Raj_deb68df204.jpg","2025-07-25T21:51:37.108Z",[],"-58","-59",{"data":1428,"meta":1429},{"id":488,"heading":483,"createdAt":489,"updatedAt":490,"publishedAt":491,"url_path_id":492,"url_path":484,"contentType":95},{},1778851853257]