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UID:ai1ec-21072@www.clsp.jhu.edu
DTSTAMP:20240329T070558Z
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
\\n\\n\\nAbstr
act
\nEmotion has intrigued researchers for generations.
This fascination has permeated the engineering community\, motivating the
development of affective computing methods. However\, human emotion remain
s notoriously difficult to accurately detect. As a result\, emotion classi
fication techniques are not always effective when deployed. This is a pro
blem because we are missing out on the potential that emotion recognition
provides: the opportunity to automatically measure an aspect of behavior t
hat provides critical insight into our health and wellbeing\, insight that
is not always easily accessible. In this talk\, I will discuss our effor
ts in developing emotion recognition approaches that are effective in natu
ral environments and demonstrate how these approaches can be used to suppo
rt mental health.
\n\nBiography
\n\nEmily Mower Provost is an Associate Professor in Comp
uter Science and Engineering and Toyota Faculty Scholar at the University
of Michigan. She received her Ph.D. in Electrical Engineering from the Uni
versity of Southern California (USC)\, Los Angeles\, CA in 2010. She has b
een 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 on the paper
\, “Say Cheese vs. Smile: Reducing Speech-Related Variability for Facial E
motion Recognition\,” winner of Best Student Paper at ACM Multimedia\, 201
4\, and a co-author of the winner of the Classifier Sub-Challenge event at
the Interspeech 2009 emotion challenge. Her research interests are in hum
an-centered speech and video processing\, multimodal interfaces design\, a
nd speech-based assistive technology. The goals of her research are motiva
ted by the complexities of the perception and expression of human behavior
.
\n
X-TAGS;LANGUAGE=en-US:2021\,December\,Mower-Provost
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-22417@www.clsp.jhu.edu
DTSTAMP:20240329T070558Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nOne of the keys to success in machine learning applic
ations is to improve each user’s personal experience via personalized mode
ls. A personalized model can be a more resource-efficient solution than a
general-purpose model\, too\, because it focuses on a particular sub-probl
em\, for which a smaller model architecture can be good enough. However\,
training a personalized model requires data from the particular test-time
user\, which are not always available due to their private nature and tech
nical challenges. Furthermore\, such data tend to be unlabeled as they can
be collected only during the test time\, once after the system is deploye
d to user devices. One could rely on the generalization power of a generic
model\, but such a model can be too computationally/spatially complex for
real-time processing in a resource-constrained device. In this talk\, I w
ill present some techniques to circumvent the lack of labeled personal dat
a in the context of speech enhancement. Our machine learning models will r
equire zero or few data samples from the test-time users\, while they can
still achieve the personalization goal. To this end\, we will investigate
modularized speech enhancement models as well as the potential of self-sup
ervised learning for personalized speech enhancement. Because our research
achieves the personalization goal in a data- and resource-efficient way\,
it is a step towards a more available and affordable AI for society.\nBio
graphy\nMinje Kim is an associate professor in the Dept. of Intelligent Sy
stems Engineering at Indiana University\, where he leads his research grou
p\, Signals and AI Group in Engineering (SAIGE). He is also an Amazon Visi
ting Academic\, consulting for Amazon Lab126. At IU\, he is affiliated wit
h various programs and labs such as Data Science\, Cognitive Science\, Dep
t. of Statistics\, and Center for Machine Learning. He earned his Ph.D. in
the Dept. of Computer Science at the University of Illinois at Urbana-Cha
mpaign. Before joining UIUC\, He worked as a researcher at ETRI\, a nation
al lab in Korea\, from 2006 to 2011. Before then\, he received his Master’
s and Bachelor’s degrees in the Dept. of Computer Science and Engineering
at POSTECH (Summa Cum Laude) and in the Division of Information and Comput
er Engineering at Ajou University (with honor) in 2006 and 2004\, respecti
vely. He is a recipient of various awards including NSF Career Award (2021
)\, IU Trustees Teaching Award (2021)\, IEEE SPS Best Paper Award (2020)\,
and Google and Starkey’s grants for outstanding student papers in ICASSP
2013 and 2014\, respectively. He is an IEEE Senior Member and also a membe
r of the IEEE Audio and Acoustic Signal Processing Technical Committee (20
18-2023). He is serving as an Associate Editor for EURASIP Journal of Audi
o\, Speech\, and Music Processing\, and as a Consulting Associate Editor f
or IEEE Open Journal of Signal Processing. He is also a reviewer\, program
committee member\, or area chair for the major machine learning and signa
l processing. He filed more than 50 patent applications as an inventor.
DTSTART;TZID=America/New_York:20221202T120000
DTEND;TZID=America/New_York:20221202T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Minje Kim (Indiana University) “Personalized Speech Enhancement: Da
ta- and Resource-Efficient Machine Learning”
URL:https://www.clsp.jhu.edu/events/minje-kim-indiana-university/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\nAbstr
act
\nOne of the keys to success in machine learning applic
ations is to improve each user’s personal experience via personalized mode
ls. A personalized model can be a more resource-efficient solution than a
general-purpose model\, too\, because it focuses on a particular sub-probl
em\, for which a smaller model architecture can be good enough. However\,
training a personalized model requires data from the particular test-time
user\, which are not always available due to their private nature and tech
nical challenges. Furthermore\, such data tend to be unlabeled as they can
be collected only during the test time\, once after the system is deploye
d to user devices. One could rely on the generalization power of a generic
model\, but such a model can be too computationally/spatially complex for
real-time processing in a resource-constrained device. In this talk\, I will present some techniques to circumvent
the lack of labeled personal data in the context of speech enhancement. Ou
r machine learning models will require zero or few data samples from the t
est-time users\, while they can still achieve the personalization goal. To
this end\, we will investigate modularized speech enhancement models as w
ell as the potential of self-supervised learning for personalized speech e
nhancement. Because our research achieves the personalization goal in a da
ta- and resource-efficient way\, it is a step towards a more available and
affordable AI for society.
\nBiography
\nMinje Kim is
an associate professor in the Dept. of Intelligent Systems Engineering at
Indiana University\, where he leads his research group\, Signals and AI Gr
oup in Engineering (SAIGE). He is also an Amazon Visiting Academic\, consu
lting for Amazon Lab126. At IU\, he is affiliated with various programs an
d labs such as Data Science\, Cognitive Science\, Dept. of Statistics\, an
d Center for Machine Learning. He earned his Ph.D. in the Dept. of Compute
r Science at the University of Illinois at Urbana-Champaign. Before joinin
g UIUC\, He worked as a researcher at ETRI\, a national lab in Korea\, fro
m 2006 to 2011. Before then\, he received his Master’s and Bachelor’s degr
ees in the Dept. of Computer Science and Engineering at POSTECH (Summa Cum
Laude) and in the Division of Information
and Computer Engineering at Ajou University (with honor) in 2006 and 2004
\, respectively. He is a recipient of various awards including NSF Career
Award (2021)\, IU Trustees Teaching Award (2021)\, IEEE SPS Best Paper Awa
rd (2020)\, and Google and Starkey’s grants for outstanding student papers
in ICASSP 2013 and 2014\, respectively. He is an IEEE Senior Member and a
lso a member of the IEEE Audio and Acoustic Signal Processing Technical Co
mmittee (2018-2023). He is serving as an Associate Editor for EURASIP Jour
nal of Audio\, Speech\, and Music Processing\, and as a Consulting Associa
te Editor for IEEE Open Journal of Signal Processing. He is also a reviewe
r\, program committee member\, or area chair for the major machine learnin
g and signal processing. He filed more than 50 patent applications as an i
nventor.
\n
X-TAGS;LANGUAGE=en-US:2022\,December\,Kim
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