BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21072@www.clsp.jhu.edu DTSTAMP:20240329T074620Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nEmotion has intrigued researchers for generations. Th is fascination has permeated the engineering community\, motivating the de velopment of affective computing methods. However\, human emotion remains notoriously difficult to accurately detect. As a result\, emotion classifi cation techniques are not always effective when deployed. This is a probl em because we are missing out on the potential that emotion recognition pr ovides: the opportunity to automatically measure an aspect of behavior tha t provides critical insight into our health and wellbeing\, insight that i s not always easily accessible. In this talk\, I will discuss our efforts in developing emotion recognition approaches that are effective in natura l environments and demonstrate how these approaches can be used to support mental health.\n\nBiography\n\nEmily Mower Provost is an Associate Profes sor in Computer Science and Engineering and Toyota Faculty Scholar at the University of Michigan. She received her Ph.D. in Electrical Engineering f rom the University of Southern California (USC)\, Los Angeles\, CA in 2010 . She has been awarded a National Science Foundation CAREER Award (2017)\, the Oscar Stern Award for Depression Research (2015)\, a National Science Foundation Graduate Research Fellowship (2004-2007). She is a co-author o n the paper\, “Say Cheese vs. Smile: Reducing Speech-Related Variability f or Facial Emotion Recognition\,” winner of Best Student Paper at ACM Multi media\, 2014\, and a co-author of the winner of the Classifier Sub-Challen ge event at the Interspeech 2009 emotion challenge. Her research interests are in human-centered speech and video processing\, multimodal interfaces design\, and speech-based assistive technology. The goals of her research are motivated by the complexities of the perception and expression of hum an behavior. DTSTART;TZID=America/New_York:20211206T120000 DTEND;TZID=America/New_York:20211206T131500 LOCATION:Maryland Hall 110 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Emily Mower-Provost (University of Michigan) “Automatically Measuri ng Emotion from Speech: New Methods to Move from the Lab to the Real World ” URL:https://www.clsp.jhu.edu/events/emily-mower-provost-university-of-michi gan/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nAdversarial attacks deceive neural network systems by adding carefully crafted perturbations to benign signals. Being almost imperceptible to humans\, these attacks pose a severe security thr eat to the state-of-the-art speech and speaker recognition systems\, makin g it vital to propose countermeasures against them. In this talk\, we focu s on 1) classification of a given adversarial attack into attack algorithm type\, threat model type\, and signal-to-adversarial-noise ratios\, 2) de veloping a novel speech denoising solution to further improve the classifi cation performance.
\nOur proposed approach uses a n x-vector network as a signature extractor to get embeddings\, which we c all signatures. These signatures contain information about the attack and can help classify different attack algorithms\, threat models\, and signal -to-adversarial-noise ratios. We demonstrate the transferability of such s ignatures to other tasks. In particular\, a signature extractor trained to classify attacks against speaker identification can also be used to class ify attacks against speaker verification and speech recognition. We also s how that signatures can be used to detect unknown attacks i.e. attacks not included during training. Lastly\, we propose to improve the signature e xtractor by making the job of the signature extractor easier by removing t he clean signal from the adversarial example (which consists of clean sign al+perturbation). We train our signature extractor using adversarial pertu rbation. At inference time\, we use a time-domain denoiser to obtain adver sarial perturbation from adversarial examples. Using our improved approach \, we show that common attacks in the literature (Fast Gradient Sign Metho d (FGSM)\, Projected Gradient Descent (PGD)\, Carlini-Wagner (CW) ) can be classified with accuracy as high as 96%. We also detect unknown attacks w ith an equal error rate (EER) of about 9%\, which is very promising.
\n X-TAGS;LANGUAGE=en-US:2022\,Joshi\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-22380@www.clsp.jhu.edu DTSTAMP:20240329T074620Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe availability of large multilingual pre-trained la nguage models has opened up exciting pathways for developing NLP technolog ies for languages with scarce resources. In this talk I will advocate for the need to go beyond the most common languages in multilingual evaluation \, and on the challenges of handling new\, unseen-during-training language s and varieties. I will also share some of my experiences with working wit h indigenous and other endangered language communities and activists.\nBio graphy\n\nAntonios Anastasopoulos is an Assistant Professor in Computer Sc ience at George Mason University. In 2019\, Antonis received his PhD in Co mputer Science from the University of Notre Dame and then worked as a post doctoral researcher at the Language Technologies Institute at Carnegie Mel lon University. His research interests revolve around computational lingui stics and natural language processing with a focus on low-resource setting s\, endangered languages\, and cross-lingual learning.\n\n\n DTSTART;TZID=America/New_York:20220930T120000 DTEND;TZID=America/New_York:20220930T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Antonios Anastasopoulos (George Mason University) “NLP Beyond the T op-100 Languages” URL:https://www.clsp.jhu.edu/events/antonis-anastasopoulos-george-mason-uni versity/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe availability of large multilingual pre-trained la nguage models has opened up exciting pathways for developing NLP technolog ies for languages with scarce resources. In this talk I will advocate for the need to go beyond the most common languages in multilingual evaluation \, and on the challenges of handling new\, unseen-during-training language s and varieties. I will also share some of my experiences with working wit h indigenous and other endangered language communities and activists.
\nBiography
\nAntonios Anastasopoulos is an Assistant Professor in Compu ter Science at George Mason University. In 2019\, Antonis received his PhD in Computer Science from the University of Notre Dame and then worked as a postdoctoral researcher at the Language Technologies Institute at Carneg ie Mellon University. His research interests revolve around computational linguistics and natural language processing with a focus on low-resource s ettings\, endangered languages\, and cross-lingual learning.
\n\n X-TAGS;LANGUAGE=en-US:2022\,Anastasopoulos\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24511@www.clsp.jhu.edu DTSTAMP:20240329T074620Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20240412T120000 DTEND;TZID=America/New_York:20240412T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Sonal Joshi (JHU) URL:https://www.clsp.jhu.edu/events/sonal-joshi-jhu/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,April\,Joshi END:VEVENT END:VCALENDAR