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DTSTART:20231105T020000
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BEGIN:VEVENT
UID:ai1ec-20730@www.clsp.jhu.edu
DTSTAMP:20240329T142316Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nRaytheon BBN participated in the IARPA MATERIAL progr
am\, whose objective is to enable rapid development of language-independen
t methods for cross-lingual information retrieval (CLIR). The challenging
CLIR task of retrieving documents written (or spoken) in one language so t
hat they satisfy an information need expressed in a different language is
exacerbated by unique challenges posed by the MATERIAL program: limited tr
aining data for automatic speech recognition and machine translation\, sca
nt lexical resources\, non-standardized orthography\, etc. Furthermore\, t
he format of the queries and the “Query-Weighted Value” performance measur
e are non-standard and not previously studied in the IR community. In this
talk\, we will describe the Raytheon BBN CLIR system\, which was successf
ul at addressing the above challenges and unique characteristics of the pr
ogram.\nBiography\n\nDamianos Karakos has been at Raytheon BBN for the pas
t nine years\, where he is currently a Senior Principal Engineer\, Researc
h. Before that\, he was research faculty at Johns Hopkins University. He h
as worked on several Government projects (e.g.\, DARPA GALE\, DARPA RATS\,
IARPA BABEL\, IARPA MATERIAL\, IARPA BETTER) and on a variety of HLT-rela
ted topics (e.g.\, speech recognition\, speech activity detection\, keywor
d search\, information retrieval). He has published more than 60 peer-revi
ewed papers. His research interests lie at the intersection of human langu
age technology and machine learning\, with an emphasis on statistical meth
ods. He obtained a PhD in Electrical Engineering from the University of Ma
ryland\, College Park\, in 2002.
DTSTART;TZID=America/New_York:20210924T120000
DTEND;TZID=America/New_York:20210924T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Damianos Karakos (Raytheon BBN) “The Raytheon BBN Cross-lingual Inf
ormation Retrieval System developed under the IARPA MATERIAL Program”
URL:https://www.clsp.jhu.edu/events/damianos-karakos/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\n\\n\\nAbstr
act
\nRaytheon BBN participated in the IARPA MATERIAL progr
am\, whose objective is to enable rapid development of language-independen
t methods for cross-lingual information retrieval (CLIR). The challenging
CLIR task of retrieving documents written (or spoken) in one language so t
hat they satisfy an information need expressed in a different language is
exacerbated by unique challenges posed by the MATERIAL program: limited tr
aining data for automatic speech recognition and machine translation\, sca
nt lexical resources\, non-standardized orthography\, etc. Furthermore\, t
he format of the queries and the “Query-Weighted Value” performance measur
e are non-standard and not previously studied in the IR community. In this
talk\, we will describe the Raytheon BBN CLIR system\, which was successf
ul at addressing the above challenges and unique characteristics of the pr
ogram.
\nBiography
\n\n
Damianos Karakos has been at Raytheon BBN for the past nine years\, wh
ere he is currently a Senior Principal Engineer\, Research. Before that\,
he was research faculty at Johns Hopkins University. He has worked on seve
ral Government projects (e.g.\, DARPA GALE\, DARPA RATS\, IARPA BABEL\, IA
RPA MATERIAL\, IARPA BETTER) and on a variety of HLT-related topics (e.g.\
, speech recognition\, speech activity detection\, keyword search\, inform
ation retrieval). He has published more than 60 peer-reviewed papers. His
research interests lie at the intersection of human language technology an
d machine learning\, with an emphasis on statistical methods. He obtained
a PhD in Electrical Engineering from the University of Maryland\, College
Park\, in 2002.
\n
\n\n
X-TAGS;LANGUAGE=en-US:2021\,Karakos\,September
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-22395@www.clsp.jhu.edu
DTSTAMP:20240329T142316Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nRecursive calls over recursive data are widely useful
for generating probability distributions\, and probabilistic programming
allows computations over these distributions to be expressed in a modular
and intuitive way. Exact inference is also useful\, but unfortunately\, ex
isting probabilistic programming languages do not perform exact inference
on recursive calls over recursive data\, forcing programmers to code many
applications manually. We introduce a probabilistic language in which a wi
de variety of recursion can be expressed naturally\, and inference carried
out exactly. For instance\, probabilistic pushdown automata and their gen
eralizations are easy to express\, and polynomial-time parsing algorithms
for them are derived automatically. We eliminate recursive data types usin
g program transformations related to defunctionalization and refunctionali
zation. These transformations are assured correct by a linear type system\
, and a successful choice of transformations\, if there is one\, is guaran
teed to be found by a greedy algorithm. I will also describe the implement
ation of this language in two phases: first\, compilation to a factor grap
h grammar\, and second\, computing the sum-product of the factor graph gra
mmar.\n\nBiography\nDavid Chiang (PhD\, University of Pennsylvania\, 2004)
is an associate professor in the Department of Computer Science and Engin
eering at the University of Notre Dame. His research is on computational m
odels for learning human languages\, particularly how to translate from on
e language to another. His work on applying formal grammars and machine le
arning to translation has been recognized with two best paper awards (at A
CL 2005 and NAACL HLT 2009). He has received research grants from DARPA\,
NSF\, Google\, and Amazon\, has served on the executive board of NAACL and
the editorial board of Computational Linguistics and JAIR\, and is curren
tly on the editorial board of Transactions of the ACL.
DTSTART;TZID=America/New_York:20221017T120000
DTEND;TZID=America/New_York:20221017T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:David Chiang (University of Notre Dame) “Exact Recursive Probabilis
tic Programming with Colin McDonald\, Darcey Riley\, Kenneth Sible (Notre
Dame) and Chung-chieh Shan (Indiana)”
URL:https://www.clsp.jhu.edu/events/david-chiang-university-of-notre-dame/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\nAbstr
act
\nRecursive calls over recursive data are w
idely useful for generating probability distributions\, and probabilistic
programming allows computations over these distributions to be expressed i
n a modular and intuitive way. Exact inference is also useful\, but unfort
unately\, existing probabilistic programming languages do not perform exac
t inference on recursive calls over recursive data\, forcing programmers t
o code many applications manually. We introduce a probabilistic language i
n which a wide variety of recursion can be expressed naturally\, and infer
ence carried out exactly. For instance\, probabilistic pushdown automata a
nd their generalizations are easy to express\, and polynomial-time parsing
algorithms for them are derived automatically. We eliminate recursive dat
a types using program transformations related to defunctionalization and r
efunctionalization. These transformations are assured correct by a linear
type system\, and a successful choice of transformations\, if there is one
\, is guaranteed to be found by a greedy algorithm. I will also describe t
he implementation of this language in two phases: first\, compilation to a
factor graph grammar\, and second\, computing the sum-product of the fact
or graph grammar.
\n\nBio
graphy
\nDavid Chiang (PhD\,
University of Pennsylvania\, 2004) is an associate professor in the Depart
ment of Computer Science and Engineering at the University of Notre Dame.
His research is on computational models for learning human languages\, par
ticularly how to translate from one language to another. His work on apply
ing formal grammars and machine learning to translation has been recognize
d with two best paper awards (at ACL 2005 and NAACL HLT 2009). He has rece
ived research grants from DARPA\, NSF\, Google\, and Amazon\, has served o
n the executive board of NAACL and the editorial board of Computational Li
nguistics and JAIR\, and is currently on the editorial board of Transactio
ns of the ACL.
\n
X-TAGS;LANGUAGE=en-US:2022\,Chiang\,October
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-23320@www.clsp.jhu.edu
DTSTAMP:20240329T142316Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nSpeech communications represents a core domain for ed
ucation\, team problem solving\, social engagement\, and business interact
ions. The ability for Speech Technology to extract layers of knowledge and
assess engagement content represents the next generation of advanced spee
ch solutions. Today\, the emergence of BIG DATA\, Machine Learning\, as we
ll as voice enabled speech systems have required the need for effective vo
ice capture and automatic speech/speaker recognition. The ability to emplo
y speech and language technology to assess human-to-human interactions off
ers new research paradigms having profound impact on assessing human inter
action. In this talk\, we will focus on big data naturalistic audio proces
sing relating to (i) child learning spaces\, and (ii) the NASA APOLLO luna
r missions. ML based technology advancements include automatic audio diari
zation\, speech recognition\, and speaker recognition. Child-Teacher based
assessment of conversational interactions are explored\, including keywor
d and “WH-word” (e.g.\, who\, what\, etc.). Diarization processing solutio
ns are applied to both classroom/learning space child speech\, as well as
massive APOLLO data. CRSS-UTDallas is expanding our original Apollo-11 cor
pus\, resulting in a massive multi-track audio processing challenge to mak
e available 150\,000hrs of Apollo mission data to be shared with science c
ommunities: (i) speech/language technology\, (ii) STEM/science and team-ba
sed researchers\, and (iii) education/historical/archiving specialists. Ou
r goals here are to provide resources which allow to better understand how
people work/learn collaboratively together. For Apollo\, to accomplish on
e of mankind’s greatest scientific/technological challenges in the last ce
ntury.\nBiography\nJohn H.L. Hansen\, received Ph.D. & M.S. degrees from G
eorgia Institute of Technology\, and B.S.E.E. from Rutgers Univ. He joined
Univ. of Texas at Dallas (UTDallas) in 2005\, where he currently serves a
s Associate Dean for Research\, Prof. of ECE\, Distinguished Univ. Chair i
n Telecom. Engineering\, and directs Center for Robust Speech Systems (CRS
S). He is an ISCA Fellow\, IEEE Fellow\, and has served as Member and TC-C
hair of IEEE Signal Proc. Society\, Speech & Language Proc. Tech. Comm.(SL
TC)\, and Technical Advisor to U.S. Delegate for NATO (IST/TG-01). He serv
ed as ISCA President (2017-21)\, continues to serve on ISCA Board (2015-23
) as Treasurer\, has supervised 99 PhD/MS thesis candidates (EE\,CE\,BME\,
TE\,CS\,Ling.\,Cog.Sci.\,Spch.Sci.\,Hear.Sci)\, was recipient of 2020 UT-D
allas Provost’s Award for Grad. PhD Research Mentoring\; author/co-author
of 865 journal/conference papers including 14 textbooks in the field of sp
eech/language/hearing processing & technology including coauthor of textbo
ok Discrete-Time Processing of Speech Signals\, (IEEE Press\, 2000)\, and
lead author of the report “The Impact of Speech Under ‘Stress’ on Military
Speech Technology\,” (NATO RTO-TR-10\, 2000). He served as Organizer\, Ch
air/Co-Chair/Tech.Chair for ISCA INTERSPEECH-2022\, IEEE ICASSP-2010\, IEE
E SLT-2014\, ISCA INTERSPEECH-2002\, and Tech. Chair for IEEE ICASSP-2024.
He received the 2022 IEEE Signal Processing Society Leo Beranek MERITORIO
US SERVICE Award.\n
DTSTART;TZID=America/New_York:20230303T120000
DTEND;TZID=America/New_York:20230303T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:John Hansen (University of Texas at Dallas) “Challenges and Advance
ments in Speaker Diarization & Recognition for Naturalistic Data Streams”
URL:https://www.clsp.jhu.edu/events/john-hansen-university-of-texas-at-dall
as/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\nAbstr
act
\nSpeech communications represents a core domain for ed
ucation\, team problem solving\, social engagement\, and business interact
ions. The ability for Speech Technology to extract layers of knowledge and
assess engagement content represents the next generation of advanced spee
ch solutions. Today\, the emergence of BIG DATA\, Machine Learning\, as we
ll as voice enabled speech systems have required the need for effective vo
ice capture and automatic speech/speaker recognition. The ability to emplo
y speech and language technology to assess human-to-human interactions off
ers new research paradigms having profound impact on assessing human inter
action. In this talk\, we will focus on big data naturalistic audio proces
sing relating to (i) child learning spaces\, and (ii) the NASA APOLLO luna
r missions. ML based technology advancements include automatic audio diari
zation\, speech recognition\, and speaker recognition. Child-Teacher based
assessment of conversational interactions are explored\, including keywor
d and “WH-word” (e.g.\, who\, what\, etc.). Diarization processing solutio
ns are applied to both classroom/learning space child speech\, as well as
massive APOLLO data. CRSS-UTDallas is expanding our original Apollo-11 cor
pus\, resulting in a massive multi-track audio processing challenge to mak
e available 150\,000hrs of Apollo mission data to be shared with science c
ommunities: (i) speech/language technology\, (ii) STEM/science and team-ba
sed researchers\, and (iii) education/historical/archiving specialists. Ou
r goals here are to provide resources which allow to better understand how
people work/learn collaboratively together. For Apollo\, to accomplish on
e of mankind’s greatest scientific/technological challenges in the last ce
ntury.
\nBiography
\nJohn H.L. Hansen\, recei
ved Ph.D. & M.S. degrees from Georgia Institute of Technology\, and B.S.E.
E. from Rutgers Univ. He joined Univ. of Texas at Dallas (UTDallas) in 200
5\, where he currently serves as Associate Dean for Research\, Prof. of EC
E\, Distinguished Univ. Chair in Telecom. Engineering\, and directs Center
for Robust Speech Systems (CRSS). He is an ISCA Fellow\, IEEE Fellow\, an
d has served as Member and TC-Chair of IEEE Signal Proc. Society\, Speech
& Language Proc. Tech. Comm.(SLTC)\, and Technical Advisor to U.S. Delegat
e for NATO (IST/TG-01). He served as ISCA President (2017-21)\, continues
to serve on ISCA Board (2015-23) as Treasurer\, has supervised 99 PhD/MS t
hesis candidates (EE\,CE\,BME\,TE\,CS\,Ling.\,Cog.Sci.\,Spch.Sci.\,Hear.Sc
i)\, was recipient of 2020 UT-Dallas Provost’s Award for Grad. PhD Researc
h Mentoring\; author/co-author of 865 journal/conference papers including
14 textbooks in the field of speech/language/hearing processing & technolo
gy including coauthor of textbook Discrete-Time Processing of Speech Signa
ls\, (IEEE Press\, 2000)\, and lead author of the report “The Impact of Sp
eech Under ‘Stress’ on Military Speech Technology\,” (NATO RTO-TR-10\, 200
0). He served as Organizer\, Chair/Co-Chair/Tech.Chair for ISCA INTERSPEEC
H-2022\, IEEE ICASSP-2010\, IEEE SLT-2014\, ISCA INTERSPEECH-2002\, and Te
ch. Chair for IEEE ICASSP-2024. He received the 2022 IEEE Signal Processin
g Society Leo Beranek MERITORIOUS SERVICE Award.
\n
\n
X-TAGS;LANGUAGE=en-US:2023\,Hansen\,March
END:VEVENT
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