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Together, these projects illustrate how advanced modeling techniques can drive more equitable, accurate, and flexible speech language models toward alignment with human values for real-world applications.\u003C/p>","2025-09-15T18:43:16.913Z","2025-10-09T14:07:20.712Z","2025-09-15T18:43:19.770Z","133",[1247],{"id":1248,"name":1249,"committee":13,"position":13,"affiliation":1250,"email":13,"biography":1251,"createdAt":1252,"updatedAt":1252,"url_path_id":1253,"contactPhoto":1254,"socialLinks":1274,"url_path":1275},65,"Hua Shen","NYU Shanghai, New York University","\u003Cp style=\"text-align:justify;\">Hua Shen is an Assistant Professor of Computer Science at NYU Shanghai, jointly affiliated with New York University. Her work anchors in HCI and intersects with multiple AI fields, such as Natural Language Processing,  Speech Processing, Machine Learning, and Data Science. Particularly, her research focuses on bidirectional human-AI alignment, aiming to empower humans to interactively explain, evaluate, and collaborate with AI, while incorporating human feedback and values to improve AI systems. She was selected as the 2023 Rising Stars of Data Science. Her research has been recognized with multiple awards, including Best Paper at AIED '24, Best Demo at CSCW’23, Best Paper Honorable Mention Award at IUI’23, and 2023 Google Research Science Conference Scholarships. She received her Ph.D. from Penn State and was previously a postdoctoral scholar at the University of Washington and the University of Michigan. More at: https://hua-shen.org.\u003C/p>","2025-09-15T18:42:35.081Z","132",{"id":1255,"name":1256,"alternativeText":13,"caption":13,"width":1257,"height":1257,"formats":1258,"hash":1270,"ext":1260,"mime":959,"size":1271,"url":1272,"previewUrl":13,"provider":41,"provider_metadata":13,"createdAt":1273,"updatedAt":1273},162,"Hua_Shen_profile_square_smaller.jpeg",697,{"small":1259,"thumbnail":1265},{"ext":1260,"url":1261,"hash":1262,"mime":959,"name":1263,"path":13,"size":1264,"width":29,"height":29},".jpeg","https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/small_Hua_Shen_profile_square_smaller_b66e229b47.jpeg","small_Hua_Shen_profile_square_smaller_b66e229b47","small_Hua_Shen_profile_square_smaller.jpeg",27.29,{"ext":1260,"url":1266,"hash":1267,"mime":959,"name":1268,"path":13,"size":1269,"width":972,"height":972},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/thumbnail_Hua_Shen_profile_square_smaller_b66e229b47.jpeg","thumbnail_Hua_Shen_profile_square_smaller_b66e229b47","thumbnail_Hua_Shen_profile_square_smaller.jpeg",4.56,"Hua_Shen_profile_square_smaller_b66e229b47",47.33,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/Hua_Shen_profile_square_smaller_b66e229b47.jpeg","2025-09-15T18:42:11.785Z",[],"-84","-85",{"id":377,"session":1278},{"id":377,"title":1279,"teaser":1280,"body":1281,"createdAt":1282,"updatedAt":1283,"publishedAt":1284,"url_path_id":1285,"contacts":1286,"url_path":1323},"Towards Robust and Explainable Evaluation of Synthesized Speech","\u003Cp style=\"text-align:justify;\">The field of speech synthesis, including text-to-speech synthesis and voice conversion, has advanced rapidly in recent years, and evaluation methodologies have been evolving as well.  Subjective human ratings collected in listening tests remain the gold standard, and online crowdsourcing platforms allow large-scale listening tests to be quickly completed by participants around the world.  However, compared to objective evaluation metrics, listening tests are more costly and time-consuming, leading researchers to consider automatic evaluation methods.\u003C/p>","\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial, Helvetica, sans-serif;\">In this talk, I will give an overview of the evaluation of synthesized speech, including subjective listening tests and objective signal-based and data-driven machine learning approaches, and the challenges of evaluating speech and modeling listener preferences.  I will also discuss the lessons learned from four years of the VoiceMOS / AudioMOS Challenge series.  Recent state-of-the-art approaches to synthetic speech quality assessment including unsupervised approaches and methods based on audio LLMs will be introduced, and future directions and remaining open challenges will be discussed.\u003C/span>\u003C/p>","2025-10-09T13:58:16.335Z","2025-10-09T13:58:18.382Z","2025-10-09T13:58:18.375Z","138",[1287],{"id":1288,"name":1289,"committee":13,"position":13,"affiliation":1290,"email":13,"biography":1291,"createdAt":1292,"updatedAt":1292,"url_path_id":1293,"contactPhoto":1294,"socialLinks":1321,"url_path":1322},66,"Erica Cooper","National Institute of Information and Communications Technology, Japan","\u003Cp style=\"text-align:justify;\">Erica Cooper received the S.B. and M.Eng. degrees from the Massachusetts Institute of Technology, USA, in 2009 and 2010, respectively, and the Ph.D. degree from Columbia University, USA, in 2019. From 2019 to 2024, she was a Postdoctoral Researcher with the National Institute of Informatics, Japan, and since April 2024, she has been a Senior Researcher with the National Institute of Information and Communications Technology, Japan. Her research interests include statistical machine learning, speech synthesis, and evaluation.  She is a founding co-organizer of the VoiceMOS/AudioMOS Challenge series and an affiliate member of the IEEE SLTC.\u003C/p>","2025-10-09T13:57:30.309Z","137",{"id":1295,"name":1296,"alternativeText":13,"caption":13,"width":1297,"height":1150,"formats":1298,"hash":1317,"ext":23,"mime":26,"size":1318,"url":1319,"previewUrl":13,"provider":41,"provider_metadata":13,"createdAt":1320,"updatedAt":1320},172,"ecooper.png",736,{"small":1299,"medium":1305,"thumbnail":1311},{"ext":23,"url":1300,"hash":1301,"mime":26,"name":1302,"path":13,"size":1303,"width":1304,"height":29},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/small_ecooper_b7926057d2.png","small_ecooper_b7926057d2","small_ecooper.png",450.6,453,{"ext":23,"url":1306,"hash":1307,"mime":26,"name":1308,"path":13,"size":1309,"width":1310,"height":791},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/medium_ecooper_b7926057d2.png","medium_ecooper_b7926057d2","medium_ecooper.png",953.38,680,{"ext":23,"url":1312,"hash":1313,"mime":26,"name":1314,"path":13,"size":1315,"width":1316,"height":972},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/thumbnail_ecooper_b7926057d2.png","thumbnail_ecooper_b7926057d2","thumbnail_ecooper.png",51.51,141,"ecooper_b7926057d2",296.54,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/ecooper_b7926057d2.png","2025-10-09T13:57:10.265Z",[],"-87","-88",{"id":524,"session":1325},{"id":524,"title":1326,"teaser":1327,"body":1328,"createdAt":1329,"updatedAt":1330,"publishedAt":1331,"url_path_id":1332,"contacts":1333,"url_path":1368},"An Evolution in Conversational AI:  From TextLLMs to SpeechLLMs","\u003Cp style=\"text-align:justify;\">Large language models (LLMs) have demonstrated remarkable ability in natural conversation, reasoning, contextual understanding and coherent generation when used with text. A natural next step is to interact with these systems through speech; the most common, natural and expressive form of human language. The traditional solution is to recognize speech with an ASR system, feed the transcript to an LLM, and generate spoken output with TTS, which is an approach that has powered spoken conversational AI for years.\u003C/p>","\u003Cp style=\"text-align:justify;\">In this talk, I will discuss the ongoing extension of LLMs toward speechLLMs to process spoken input directly and integrate speech and language understanding in a unified framework. I will introduce representative architectures showing how speech and language components can be brought together.  I then give an overview of key tasks formulated as a unified data structure of triplets comprising audio, instruction, and response. From there, I will sketch the main directions for current research and development in this area, including modeling strategies, data construction, evaluation methodologies, and considerations for operational deployment.\u003Cbr>Drawing on practical insights gained from several spoken language understanding tasks, I will highlight what works well in practice, what remains challenging, and where speechLLMs show clear advantages over cascaded systems. I conclude with a preview of how these developments could pave the way toward spoken language system that are more integrated, accurate, low-latency, and context-aware than those currently in use.\u003C/p>","2025-10-15T18:43:20.492Z","2025-10-15T18:43:22.665Z","2025-10-15T18:43:22.657Z","147",[1334],{"id":548,"name":1335,"committee":13,"position":13,"affiliation":1336,"email":13,"biography":1337,"createdAt":1338,"updatedAt":1338,"url_path_id":1339,"contactPhoto":1340,"socialLinks":1366,"url_path":1367},"Kadri Hacioglu","Uniphore, USA","\u003Cp style=\"text-align:justify;\">Kadri Hacıoğlu is a Principal AI Scientist at Uniphore, working on product-oriented research and development in speech recognition, natural language processing (NLP), and conversational AI. His current work focuses on end-to-end speech and multimodal language models, knowledge distillation for specialized large language models, and scalable architectures for real-time speech applications.\u003Cbr>Kadri received his Ph.D. for work on novel speech signal representations and low-bit-rate transmission methods and previously taught at Eastern Mediterranean University, Cyprus, focusing on intelligent signal processing and communications.  He subsequently was a research faculty member at the University of Colorado at Boulder and the Center for Spoken Language Research,  where he contributed to research in speech recognition, information extraction, semantic role labeling/parsing and machine learning for NLP.  He co-developed the SONIC speech recognition engine and advanced methods that integrated semantic and discourse information for improved recognition accuracy. His contributions also included developing NLP tools for text processing and leading teams demonstrating competitive results in government-run and academic speech and NLP evaluations. He later worked on speech-enabled language learning technologies at Rosetta Stone and spoken language systems for consumer devices at Sensory Inc.\u003C/p>","2025-10-15T18:00:11.323Z","146",{"id":1087,"name":1341,"alternativeText":13,"caption":13,"width":1342,"height":1343,"formats":1344,"hash":1362,"ext":1260,"mime":959,"size":1363,"url":1364,"previewUrl":13,"provider":41,"provider_metadata":13,"createdAt":1365,"updatedAt":1365},"kadrihacioglu.jpeg",752,831,{"small":1345,"medium":1351,"thumbnail":1357},{"ext":1260,"url":1346,"hash":1347,"mime":959,"name":1348,"path":13,"size":1349,"width":1350,"height":29},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/small_kadrihacioglu_ff5c8b14c3.jpeg","small_kadrihacioglu_ff5c8b14c3","small_kadrihacioglu.jpeg",29.46,452,{"ext":1260,"url":1352,"hash":1353,"mime":959,"name":1354,"path":13,"size":1355,"width":1356,"height":791},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/medium_kadrihacioglu_ff5c8b14c3.jpeg","medium_kadrihacioglu_ff5c8b14c3","medium_kadrihacioglu.jpeg",64.66,679,{"ext":1260,"url":1358,"hash":1359,"mime":959,"name":1360,"path":13,"size":1361,"width":780,"height":972},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/thumbnail_kadrihacioglu_ff5c8b14c3.jpeg","thumbnail_kadrihacioglu_ff5c8b14c3","thumbnail_kadrihacioglu.jpeg",3.68,"kadrihacioglu_ff5c8b14c3",77.46,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/asru25/kadrihacioglu_ff5c8b14c3.jpeg","2025-10-15T17:59:43.003Z",[],"-94","-95",{"data":1370,"meta":1371},{"id":501,"heading":502,"createdAt":507,"updatedAt":508,"publishedAt":509,"url_path_id":510,"url_path":503,"contentType":95},{},1778851853257]