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UID:ai1ec-21275@www.clsp.jhu.edu
DTSTAMP:20240328T200110Z
CATEGORIES;LANGUAGE=en-US:Student Seminars
CONTACT:
DESCRIPTION:Abstract\n\n\n\nAutomatic discovery of phone or word-like units
is one of the core objectives in zero-resource speech processing. Recent
attempts employ contrastive predictive coding (CPC)\, where the model lear
ns representations by predicting the next frame given past context. Howeve
r\, CPC only looks at the audio signal’s structure at the frame level. The
speech structure exists beyond frame-level\, i.e.\, at phone level or eve
n higher. We propose a segmental contrastive predictive coding (SCPC) fram
ework to learn from the signal structure at both the frame and phone level
s.\n\nSCPC is a hierarchical model with three stages trained in an end-to-
end manner. In the first stage\, the model predicts future feature frames
and extracts frame-level representation from the raw waveform. In the seco
nd stage\, a differentiable boundary detector finds variable-length segmen
ts. In the last stage\, the model predicts future segments to learn segmen
t representations. Experiments show that our model outperforms existing ph
one and word segmentation methods on TIMIT and Buckeye datasets.
DTSTART;TZID=America/New_York:20220211T120000
DTEND;TZID=America/New_York:20220211T131500
LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Student Seminar – Saurabhchand Bhati “Segmental Contrastive Predict
ive Coding for Unsupervised Acoustic Segmentation”
URL:https://www.clsp.jhu.edu/events/student-seminar-saurabhchand-bhati/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\n\\n\\nAbstr
act
\n\n
\n\n
Automatic discovery of phone or word-like units is one
of the core objectives in zero-resource speech processing. Recent attempts
employ contrastive predictive coding (CPC)\, where the model learns repre
sentations by predicting the next frame given past context. However\, CPC
only looks at the audio signal’s structure at the frame level. The speech
structure exists beyond frame-level\, i.e.\, at phone level or even higher
. We propose a segmental contrastive predictive coding (SCPC) framework to
learn from the signal structure at both the frame and phone levels.\n
\n
SCPC is a hierarchical mode
l with three stages trained in an end-to-end manner. In the first stage\,
the model predicts future feature frames and extracts frame-level represen
tation from the raw waveform. In the second stage\, a differentiable bound
ary detector finds variable-length segments. In the last stage\, the model
predicts future segments to learn segment representations. Experiments sh
ow that our model outperforms existing phone and word segmentation methods
on TIMIT and Buckeye datasets.
\n
\n
\n
\n
X-TAGS;LANGUAGE=en-US:2022\,Bhati\,Februray
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-22417@www.clsp.jhu.edu
DTSTAMP:20240328T200110Z
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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