Conf: INTERSPEECH 2014, Keynote spekers, September 14-18, 2014, Singapore
Thierry Hamon
hamon at LIMSI.FR
Wed Jun 25 08:36:19 UTC 2014
Date: Mon, 23 Jun 2014 14:08:03 +0800
From: "Organization @ Interspeech 2014" <organization at interspeech2014.org>
Message-ID: <53A7C443.8060302 at interspeech2014.org>
X-url: http://www.interspeech2014.org
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- INTERSPEECH 2014 - SINGAPORE -
- September 14-18, 2014 -
- http://www.interspeech2014.org -
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ISCA, COLIPS and the organizing Committee of INTERSPEECH 2014 are proud
to announce that INTERSPEECH 2014 will feature five plenary talks by
internationally renowned experts.
- keynote speech
by the ISCA Medallist 2014
- "Decision Learning in Data Science:
Where John Nash Meets Social Media"
by Professor K. J. Ray Liu
- "Language Diversity: Speech Processing In A Multi-Lingual Context"
by Dr. Lori Lamel
- "Sound Patterns In Language"
by Professor William Shi-Yuan WANG 王士元
- "Achievements and Challenges of Deep Learning
From Speech Analysis And Recognition To Language
And Multimodal Processing"
by Dr. Li DENG
Details of the keynote speeches and biographies of the presenters are
given below.
Looking forward to welcome you in Singapore,
the organizing committee
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* On Monday, 15th of September *
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The ISCA Medallist 2014 will give a keynote speech.
The name of the Medallist and subject of the talk will be
disclosed on the first day of INTERSPEECH 2014.
************************************************************************
* On Tuesday morning, 16th of September *
************************************************************************
Professor K. J. Ray Liu
Department of Electrical and Computer Engineering
University of Maryland, College Park
will give a presentation on:
"Decision Learning in Data Science: Where John Nash Meets Social Media"
Abstract
With the increasing ubiquity and power of mobile devices, as well
as the prevalence of social media, more and more activities in our
daily life are being recorded, tracked, and shared, creating the
notion of “social media”. Such abundant and still growing real
life data, known as “big data”, provide a tremendous research
opportunity in many fields.
To analyze, learn and understand such user-generated big data,
machine learning has been an important tool and various
machine learning algorithms have been developed.
However, since the user-generated big data is the outcome of users’
decisions, actions and their socio-economic interactions, which are
highly dynamic, without considering users’ local behaviours and
interests, existing learning approaches tend to focus on optimizing
a global objective function at the macroeconomic level, while
totally ignore users’ local decisions at the micro-economic
level. As such there is a growing need in bridging machine/social
learning with strategic decision making, which are two
traditionally distinct research disciplines, to be able to jointly
consider both global phenomenon and local effects to
understand/model/analyze better the newly arising issues in the
emerging social media. In this talk, we present the notion of
“decision learning” that can involve users's behaviours and
interactions by combining learning with strategic decision making.
We will discuss some examples from social media with real data to
show how decision learning can be used to better analyze users’
optimal decision from a user’ perspective as well as design a
mechanism from the system designer’s perspective to achieve a
desirable outcome.
Biography of the speaker
Dr. K. J. Ray Liu was named a Distinguished Scholar-Teacher of
University of Maryland in 2007, where he is Christine Kim Eminent
Professor of Information Technology.
He leads the Maryland Signals and Information Group conducting
research encompassing broad areas of signal processing and
communications with recent focus on cooperative communications,
cognitive networking, social learning and decision making, and
information forensics and security. Dr. Liu has received numerous
honours and awards including IEEE Signal Processing Society 2009
Technical Achievement Award and various best paper awards from IEEE
Signal Processing, Communications, and Vehicular Technology
Societies, and EURASIP. A Fellow of the IEEE and AAAS, he is
recognized by Thomson Reuters as an ISI Highly Cited Researcher.
Dr. Liu was the President of IEEE Signal Processing Society, the
Editor-in-Chief of IEEE Signal Processing Magazine and the founding
Editor-in-Chief of EURASIP Journal on Advances in Signal
Processing. Dr. Liu also received various research and teaching
recognitions from the University of Maryland, including Poole and
Kent Senior Faculty Teaching Award, Outstanding Faculty Research
Award, and Outstanding Faculty Service Award, all from A. James
Clark School of Engineering; and Invention of the Year Award (three
times) from Office of Technology Commercialization.
************************************************************************
* On Tuesday afternoon, 16th of September *
************************************************************************
Dr. Lori Lamel
Senior Research scientist (DR1), LIMSI-CNRS
will give a presentation on
"Language Diversity: Speech Processing In A Multi-Lingual Context"
Abstract
Speech processing encompasses a variety of technologies
that automatically process speech for some downstream processing.
These technologies include identifying the language or dialect
spoken, the person speaking, what is said and how it is said. The
downstream processing may be limited to a transcription or to a
transcription enhanced with additional meta-data, or may be used to
carry out an action or interpreted within a spoken dialogue system
or more generally for analytics. With the availability of large
spoken multimedia or multimodal data there is growing interest in
using such technologies to provide structure and random access to
particular segments. Automatic tools can also serve to annotate
large corpora for exploitation in linguistic studies of spoken
language, such as acoustic-phonetics, pronunciation variation and
diachronic evolution, permitting the validation of hypotheses and
models.
In this talk I will present some of my experience with speech
processing in multiple languages, drawing upon progress in the
context of several research projects, most recently the Quaero
program and the IARPA Babel program, both of which address the
development of technologies in a variety of languages, with the aim
to some highlight recent research directions and challenges.
Biography of the speaker
I am a senior research scientist (DR1) at the CNRS, which I joined
as a permanent researcher at LIMSI in October 1991.
I received my Ph.D. degree in Electrical Engineering and Computer
Science in May 1988 from the Massachusetts Institute of Technology.
My research activities focus on large vocabulary speaker-
independent, continuous speech recognition in multiple languages
with a recent focus on low-resourced languages; lightly and
unsupervised acoustic model training methods; studies in acoustic-
phonetics; lexical and pronunciation modelling. I contributed to
the design, and realization of large speech corpora (TIMIT, BREF,
TED). I have been actively involved in the research projects, most
recently leading the activities on speech processing in the OSEO
Quaero program, and I am currently co-principal investigator for
LIMSI as part of the IARPA Babel Babelon team led by BBN.
I served on the Steering committee for Interspeech 2013 as
co-technical program chair along with Pascal Perrier, and I am now
serving on the Technical Program Committee of Interspeech 2014.
************************************************************************
* On Wednesday, 17th of September *
************************************************************************
Professor William Shi-Yuan WANG 王士元
Centre for Language and Human Complexity,
Chinese University of Hong Kong
Professor Emeritus, University of California at Berkeley
Honorary Professor, Peking University
Academician, Academia Sinica
will give a presentation about
"Sound Patterns In Language"
Abstract
In contrast to other species, humans are unique in having developed
thousands of diverse languages which are not mutually
intelligible. However, any infant can learn any language with ease,
because all languages are based upon common biological
infrastructures of sensori-motor, memorial, and cognitive
faculties. While languages may differ significantly in the sounds
they use, the overall organization is largely the same.
It is divided into a discrete segmental system for building words
and a continuous prosodic system for expressing, phrasing,
attitudes, and emotions. Within this organization, I will discuss a
class of languages called 'tone languages', which makes special use
of F0 to build words. Although the best known of these is Chinese,
tone languages are found in many parts of the world, and operate on
different principles. I will also comment on relations between
sound patterns in language and sound patterns in music, the two
worlds of sound universal to our species.
Biography of the speaker
William S-Y. Wang received his early schooling in China, and his
PhD from the University of Michigan. He was appointed Professor of
Linguistics at the University of California at Berkeley in 1965,
and taught there for 30 years.
Currently he is in the Department of Electronic Engineering and in
the Department of Linguistics and Modern Languages of the Chinese
University of Hong Kong, and Director of the newly established
Joint Research Centre for Language and Human Complexity. His
primary interest is the evolution of language from a multi-
disciplinary perspective.
************************************************************************
* On Thursday, 18th of September *
************************************************************************
Dr. Li DENG
Principal Researcher and Research Manager
Deep Learning Technology Centre,
Microsoft Research, Redmond, USA
will give a presentation on the
"Achievements and Challenges of Deep Learning
From Speech Analysis And Recognition To Language And Multimodal
Processing"
Abstract
Artificial neural networks have been around for over half a century
and their applications to speech processing have been almost as
long, yet it was not until year 2010 that their real impact had
been made by a deep form of such networks, built upon part of the
earlier work on (shallow) neural nets and (deep) graphical models
developed by both speech and machine learning communities. This
keynote will first reflect on the path to this transformative
success, sparked by speech analysis using deep learning methods on
spectrogram-like raw features and then progressing rapidly to
speech recognition with increasingly larger vocabularies and scale.
The role of well-timed academic-industrial collaboration will be
highlighted, so will be the advances of big data, big compute, and
the seamless integration between the application-domain knowledge
of speech and general principles of deep learning. Then, an
overview will be given on sweeping achievements of deep learning in
speech recognition since its initial success in 2010 (as well as in
image recognition and computer vision since 2012). Such
achievements have resulted in across-the-board, industry-wide
deployment of deep learning. The final part of the talk will look
ahead towards stimulating new challenges of deep learning ---
making intelligent machines capable of not only hearing (speech)
and seeing (vision), but also of thinking with a “mind”; i.e.
reasoning and inference over complex, hierarchical relationships
and knowledge sources that comprise a vast number of entities and
semantic concepts in the real world based in part on multi- sensory
data from the user. To this end, language and multimodal
processing --- joint exploitation and learning from text,
speech/audio, and image/video --- is evolving into a new frontier
of deep learning, beginning to be embraced by a mixture of research
communities including speech and spoken language processing,
natural language processing, computer vision, machine learning,
information retrieval, cognitive science, artificial intelligence,
and data/knowledge management. A review of recent published studies
will be provided on deep learning applied to selected language and
multimodal processing tasks, with a trace back to the relevant
early connectionist modelling and neural network literature and
with future directions in this new exciting deep learning frontier
discussed and analyzed.
Biography of the speaker
Li Deng received Ph.D. from the University of Wisconsin-Madison.
He was a tenured professor (1989-1999) at the University of
Waterloo, Ontario, Canada, and then joined Microsoft Research,
Redmond, where he is currently a Principal Research Manager of its
Deep Learning Technology Centre.
Since 2000, he has also been an affiliate full professor at the
University of Washington, Seattle, teaching computer speech
processing. He has been granted over 60 US or international
patents, and has received numerous awards and honours bestowed by
IEEE, ISCA, ASA, and Microsoft including the latest IEEE SPS Best
Paper Award (2013) on deep neural nets for speech recognition. He
authored or co-authored 4 books including the latest one on Deep
Learning: Methods and Applications. He is a Fellow of the
Acoustical Society of America, a Fellow of the IEEE, and a Fellow
of the ISCA. He served as the Editor-in-Chief for IEEE Signal
Processing Magazine (2009-2011), and currently as Editor-in-Chief
for IEEE Transactions on Audio, Speech and Language Processing. His
recent research interests and activities have been focused on deep
learning and machine intelligence applied to large-scale text
analysis and to speech/language/image multimodal processing,
advancing his earlier work with collaborators on speech analysis
and recognition using deep neural networks since 2009.
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