Selected Papers

The World of Mushrooms—A Transdisciplinary Approach to
Human-Computer Interaction with Ambient Intelligence
Eisaku Maeda†, Yasuhiro Minami, Masato Miyoshi,
Minako Sawaki, Hiroshi Sawada, Atsushi Nakamura,
Junji Yamato, Takeshi Yamada, and Ryuichiro Higashinaka
Abstract
This paper provides an update to the January 2006 letter on “ambient intelligence”, describing thisyear’s achievements and the latest demonstration systems. The Ambient Intelligence Project (also known
by the code name project Mushroom) aims to bridge the boundaries between technological fields andthus cover the entire field of communication science.

1. Introduction
NTT Communication Science Laboratories (NTTCS Labs.) initiated a research project with the themeof “ambient intelligence” (kankyo chinou in Japanese) in October 2004. It is being primarily undertaken by the Ambient Intelligence Research Group andthe Intelligence Integration Open Laboratory of NTT
CS Labs. Kankyo chinou has now been officiallyadopted as the main vision of the Labs. It has two
main purposes: the first is to create new lifestyles
through the research and development of communication science and technologies based on the conceptof ambient intelligence, and the second is to break
down the barriers surrounding existing research disciplines and to strategically develop the transdisciplinary area, as symbolized by the term “intelligence
integration”.
Last year we presented the key concepts of ambient
intelligence and demonstrated a prototype at OpenHouse 2005 of our laboratories [1]. A letter in theJanuary 2006 issue of NTT Technical Review introduced these concepts and achievements to a wider,international audience [2]. We also discussed thelifestyles that could be made possible by ambient† NTT Communication Science Laboratories
Atsugi-shi, 243-0198 Japan
Email: maeda@cslab.kecl.ntt.co.jp

Vol. 4 No. 12 Dec. 2006

intelligence and suggested the specific issues to be
tackled toward the achievement of ambient intelligence in a published paper [3].
In this paper, we review the background of the
Ambient Intelligence Project in detail for the convenience of new readers who missed the previous letter[2] because the concepts are important but probably
unfamiliar. We then go on to outline the project’s
achievements to date.
2. Ambient intelligence by information andcommunication science technology
Information science was born at the beginning of
the 20th century and has grown remarkably. The
communication science technologies of Japan haveadvanced significantly together with the Japanese
economy, bringing significant benefits to our lives
today. However, it is difficult to expect a similar rateof growth in the coming 50 years. Japan’s low
birthrate and longevity are changing the nation’s
demographic structure and causing a labor shortage.
This will increase the burden of social welfare. Furthermore, global issues such as global warming, energy shortfalls, and waste disposal problems will only
become more serious. In these circumstances, oursociety is searching for major qualitative changes.
In these social circumstances, the roles of communication science in enriching our lives have changed.
17Selected Papers

Mental stability and satisfaction rather than physicalconvenience have become new targets. While we areaccustomed to depending on doctors and religion forrelief in times of difficulty, we believe that the majority of people are looking for a more immediate andcasual environment to ease some of their anxieties
and enhance the vitality of daily life. Our long term
goal is to apply communication science technologies
to create such an environment.
In achieving the environment we envisage, one key
word is communication because it is nothing less thanthe exchange of human kindness. Today, the Internet
is increasingly popular as a medium for exchanging
information between individuals, and new communities are created every day. The Internet enables anenormous amount of communication beyond thephysical limits imposed in daily life. Yet no matter
how much Internet communication is developed,
people cannot dispel the sense of isolation unless they
can receive the same mental support that they getfrom their family and friends, who share in theirmemories and knowledge. The popularity of communication over the Internet gives us a renewed recognition of the importance of direct communication inshared spaces without using networks.
For example, the casual remark or subtle smilecommon in direct communication can enrich ourlives. We may not be conscious of this in most situations, but we often find out later that even small
events are essential to our wellbeing. This support
can also be achieved by communication with entities
outside our everyday experience. We believe in harnessing the power of what we call “fairies and goblins”, as explained in the following section. We anticipate that communication science technologies willusher in the world of fairies and goblins within thenext 50 years.
“Ambient intelligence” implies intelligence thatsurrounds us. The term ambient intelligence is also
used for research being pursued in the EU and theUSA [4]-[6]. Those studies aim to produce a design
for a future information society by making use ofcomputers and sensors embedded in the environmentso as to create more natural and intelligent interfaces.
Terms such as “ubiquitous” or “pervasive” comeclose to describing the concept. However, such R&D
studies tend to focus on computers and sensors, andas such are geared toward hardware or devices.
On the other hand, our Ambient Intelligence Project is focused more on such things as hearing, vision,
language, and knowledge, which are all related tohuman intelligence. The aim is to discover what kind18of communication we really need and to achieve it.
This is where our “ambient intelligence” differs fromsuch concepts as ubiquitous computing or conventional ambient intelligence. We plan to achieve ambient intelligence by using information and communication science technologies. That is, we will use
NTT’s areas of expertise—speech, sound, language,
dialogue, vision, data retrieval, networking, and soon—to create a new style of ambient intelligence thatplaces human intelligence and intellect at the forefront. That will lead to a proposal about a future
lifestyle and clarify specific issues to be tackled
towards it [3]. Furthermore, “ambient intelligence”
may drive a robot brain or be embedded in the environment of ubiquitous computers and networks.
3. Fairies and goblins“Ambient intelligence” is premised on the creationof communication partners that we want constantly at
our side. To develop this concept, we started by considering appropriate fundamental features. For example, such entities must have different personalities
and be able to evolve, grow, and express their feelings. They might seem to have the same level of intelligence as precocious children. They hide nearby andare unobtrusive, unassertive, and even a little mischievous. They respond when called and are visible tosome people but not to others.
This world of “ambient intelligence” is rather similar to the world of fairies and goblins, which is familiar in both the East and the West. This makes “fairies
and goblins” an adequate concept on which to base acommon understanding of ambient intelligence.
Ambient intelligence also provides a means for communication that speaks to the heart, as fairies andgoblins used to do. Since information technologies
have advanced so much, the time is ripe for thinking
about new ways of using them in our daily lives astools for refreshing the heart and mind. This approach
will, we believe, make information technologies
desirable to the general public.
We gave the name “mushrooms” to the fairies andgoblins that are our idealized vision of ambient intelligence. These mushrooms live in a world called“Mush-Room”. Another important issue in terms ofconcept design is the evolution of the mushrooms.
The process and the future perspective of mushroom
evolution (Fig. 1) represent the progress of the R&D
itself and a road map for the future of “ambient intelligence”. As mushrooms evolve, they will approachhuman intelligence or intellect. The world of “ambiNTT Technical Review

Selected Papers

Origin of mushrooms
Unknown

Speak

Deep
knowledge

ListenBroadknowledge

Speak

Deep
knowledge

ListenBroadknowledge

Mushrooms 2005
Unknown

SearchMoochaChachaKeekieMookieSheeshieMushrooms 2006
Unknown

Speak

Deep
knowledge

ListenBroadknowledge
See

SearchCahmooChacha2006

Whowhoo
SearcharSearchie
Keekie2006
Sheeshie2006

Fig. 1. Evolution of mushrooms.

ent intelligence” cannot be produced simply byadvances in this or that technology. That is why werequire intelligence integration that covers the whole
field of communication science.
4. The world of mushrooms 2006
To achieve the ideal world of ambient intelligence,
it is important to provide missing technologies aswell as to enhance the current information processing
technologies. This year’s Open House (officiallycalled Mirai-Soron 2006) offered three demonstraVol. 4 No. 12 Dec. 2006

tions (Fig. 2) in line with three fundamental conceptsof ambient intelligence: watching over, keep looking,and hiding and other actions. They well illustrate ourresearch achievements of this year [7]. These demonstration systems were implemented and are being
maintained mainly by K. Shimomura, T. Matsuda, Y.
Kashihara, and K. Maeda.
4.1 Demonstration A: mushrooms that watch,
notice, listen, and answer questions
The keywords for this demonstration are watching
over, noticing, and listening and answering questions.
19Selected Papers

CahmooTarget speech
Visitor(query)CahmooSpeech input
Image input
Where will the next
(microphone)(camera)Olympics be held?
Noise training control
Blind source separation
Face detection
Separated
KeekieDetection
Recognition start/stopspeechresultIn Beijing,
2006
Animation
China
Extremely large
Integration andcontrol
vocabulary speech
control modules
recognition
ResultAcoustic andSheeshie
Japanese question
language models
2006
Sheeshieanswering
2006
Answer14-year news
Speech synthesis
database
Display
Control flowChild speech
KeekieSpeech output
Signal flow
database
2006
Data reference
Voice and noise(A)

Target image

I know
this song!

Target soundSearchie

Searchar

Retrieved soundSound input
and image(mic.)
Sound andImage search control module
Sound search control module
image database
Image featureSound feature
extraction
extraction
SearchSearch result
resultImage features
Sound features
Search engine
Feature matching andHigh similarity
similarity calculation
time detection
ResultHigh similarity part ThresholdSound and
image
Content A
Content B
Content C
featuredata…Time
Signal flow
Data reference
Quick media search technique
Retrieved soundand image

Image input(camera)Similarity

Searchar(B)

He was a feudal warlord.
Akechi
Mitsuhide.
Almost.

Whowhoo

Chacha2006

entityNamed entity Named
extraction
data
Microphone
Acoustic andlanguage models
Search engine

KeekieDescriptionsof people
Definitionalquestion answering(C)

WWW: world wide web

WWW

News data
and WWW

Animation
control

ChachaWhowhoo 2006

Mushroom animation Display
ResultIntegration, control,
Extremely large
and dialogue modules
vocabulary speech Hints
recognition
Hint ranking
Automatic hint
Distance
Speech synthesis
generation
Visualization of
Network mining
people network
People network
Control flow
Signal flow
Data reference

WhowhooInside of Whowhoo’s brain

Fig. 2. Overviews of three demonstrations presented in Laboratory Open House 2006 (Mirai-Soron 2006).
The illustrations depict the demonstrations (left) and schematic block diagrams of their systems (right).

This demonstration was based on the following scenario. Mushrooms are watching and waiting to help
people sharing a common space. A mushroom
20

notices that person A would like to chat with person
B (process I). It interacts with A through speech toconfirm the situation (process II) and helps overcome
NTT Technical Review

Selected Papers

the problems that person A faces in communicatingwith B.
This demonstration uses several mushrooms. Forprocess I, the mushroom called Cahmoo is equippedwith a video camera and face detection technology*1.
When a person gets close to Cahmoo and faces its
camera, Cahmoo assumes that the person wants tochat and tries to start a conversation by saying“Hello” or “What’s up?” in Japanese. Simultaneously, the mushroom called Keekie, which uses four
microphones as its ears, suppresses environmental
noise and the voices coming from other speakers inorder to emphasize the speech from the interlocutor.
This preparation for the next stage consisting of question answering is based on a combination of blindaudio source separation*2 and face detection technologies.
For process II, Keekie recognizes the interlocutor’s
speech and transmits the query to the mushroom
called Sheeshie. Sheeshie looks for answers in 14
years’ worth of computerized newspaper articles(1991 to 2004) and finally answers the query by using
speech synthesis technology. Such a speech-driven
question answering system uses extremely large
vocabulary speech recognition *3 and question
answering*4 technology. Keekie can quickly andaccurately recognize queries expressed in various
ways. Moreover, while answering the query, Cahmoo
keeps watching the interlocutor to guess whether
he/she wants to pose another query. This new faculty*1 Face detection technology: Cahmoo has a camerathat captures images and sends them to a computer.
The computer detects the position and size of ahuman face in the images. The face detection speed
is accelerated by using two-step processing. First,
through a comparison of averaged pixel values inneighboring areas, most candidate areas in theimages that clearly do not correspond to a face areremoved [5]. The remaining areas are then analyzed
in detail by an assessment of their pixel values. In
Demonstration A, the results of face detection wereused to improve the performance of blind audio
source separation*2 and speech recognition*3.
*2 Blind audio source separation technology: Keekie
has an array of four microphones. By using only thesignals captured by these microphones, it controls
the array directivity to discriminate between target
speech and undesired sounds, and then suppresses
the undesired sounds. Such processing can be performed in a blind manner, i.e., without any a priori
information about the source direction [9]. Keekie
uses additional information provided by Cahmoo toachieve higher audio discrimination performance.
When no face is detected, the captured signal is considered to consist of just undesired sounds and the
directions of those sounds are estimated. If a face is
detected, the probability of hearing some speech
becomes high, and the target speech is extracted byremoving the undesired sounds using the previously obtained information (their directions). This
allows Keekie to hear the speech clearly even in anoisy environment such as an exhibition hall.
*3 Extremely large vocabulary speech recognition
technology: Keekie has a SOLON speech recognizer [10], which features an algorithm based on fast
on-the-fly composition for weighted finite-statetransducer type advanced computational models.
Due to its accuracy and efficiency, the algorithm
enables, for the first time in the world, realtime
speech recognition with a vocabulary of several
million words [8]. This technology makes it possible to handle a very wide variety of expressions
including not only common vocabulary words but
also personal names, organization names, place
names, and newly coined words.
Moreover, using information provided by the facedetection technology, Keekie performs speech
recognition only when Cahmoo detects a face.
When we speak with someone else, we use not only
the information contained in speech utterances, butalso other information provided by gesture or face
direction. For example, by watching the direction inwhich the other person is looking, we may recognize that he/she is talking to himself/herself. In suchcases, we usually do not answer the interlocutor. Ina similar way, Keekie uses the face direction information extracted by Cahmoo. We believe that withthis architecture we have made a significant step
toward more natural human-machine speech communication.
*4 Open domain question answering (QA): One of
the most important features of ambient intelligenceis its ability to conduct natural speech dialogues
with humans. Our laboratories have been working
on speech dialogue systems and QA systems foryears [12], [13], and this demonstration uses our QA
technology for “definitional questions” such as“Who is Claude Monet?” Our QA system answers
this question by locating various bits of information
about the person from numerous documents.
Whowhoo uses this technology not to answer questions but to generate hints.

Vol. 4 No. 12 Dec. 200621

Selected Papers

is achieved by combining speech recognition andface detection technologies. The combination of facedetection, blind audio source separation, and speech
recognition technologies makes these mushrooms a
bit smarter.

release date.
Searchie and Searchar use small video cameras aseyes and microphones as ears. Nowadays, it is not difficult to capture images from video cameras as digital information and sound from microphones. Modern large-capacity hard disk drives can now store several years’ worth of data. The challenge is to find theinformation you want from among the huge numberof sound and image files. Stored information cannot
be used just by watching and listening without using
a search method, i.e., a media search technology.
Mushrooms watch TV and they instantly search huge
databases for a variety of information about current
TV programs, such as the title of music being played
or information about whether or not the scene wasused before. Even if images are distorted or intermittent or if the sound is noisy or interrupted, accurate
searches remain possible.

4.2 Demonstration B: mushrooms that watch,
listen, and searchHere, we introduce the mushrooms Searchie andSearchar. They watch and listen to things aroundthem all the time and then find what someone wantsfrom a tiny clue. These functions are achieved bymedia search technologies*5 developed over the lastten years at NTT CS Labs. In last year’s “The Worldof Mushrooms” [1], we introduced Mookie andMoocha, which search for music based on short clips.
They use audio search technology and a music database to reply with the name of a song that is being
played. This year, we unveiled Searchie and Searchar,which can instantly locate the original image or audio
file in a huge database based on small image segments or sound clips. They use video and audio
search technologies and databases of music, music
video clips, and TV programs. They provide the user
with information such as the program title and its

4.3 Demonstration C: quiz-master mushroom
In this demonstration, we can learn new things subconsciously through a casual conversation with themushrooms. The Whowhoo mushrooms engage inrepartee in the form of a guessing game. They usually hide, but can occasionally pop up and quiz each

*5 Media search technology: In researching media
search technology, we have concentrated on three
key factors: rapid, robust, and smart. Rapid is themost basic characteristic of this technology [14],[15]. Indeed, our search technology can locate specific query matches in huge video and sound clip
databases at extremely high speeds; for example, a
database containing several hundred hours of material can be searched in one second. This key featureenables us to scan a huge database of hundreds ofthousands of music pieces to find the name of background music or to scan a TV program database tofind the specific scene in a drama.
Robust is another basic characteristic. Most
media data is not cleanly observable in the realworld. We have to catch the music in the presenceof noise or recognize it in the presence of the distortion created by microphones/speakers and codecs
used for transmission or archiving. We have to recognize video with occlusion, geometrical distortion,or color shifts. Robustness against noise and distortion is very important if media search technology is
to lead to practical applications in the real world. To
date, we have implemented a music search withmobile phone transmission [16] and a backgroundmusic search in the presence of loud speech or othersounds [17]. We have also made a video search thatis robust against partial occlusion and color shifts
caused by camera/display characteristics.
The third characteristic, smart, is a little tricky. As
explained above, robust targets changes caused by
the external environment. Here, we would like tohandle the changes in the target itself. For example,
we would like to find the same object, not the same
scene, even if the object was captured from a different angle. Taking a music search as an example, wewould like to find the same musical score eventhough it was performed by a different person using
a different instrument. To achieve these types of
media search, we need a technique for modelingobjects or music that parameterizes the possible
changes, for example, in viewing angle or tone colorof sounds. This is crucial for mushrooms, which
will live with us for a long time and record all thehappenings in our lives and then retrieve relevant
information at the appropriate time and in the appropriate manner to help us become happy. Researchon modeling in media searches is an important
theme and we have already made significant
progress.

22

NTT Technical Review

Selected Papers

other. For example:
Whowhoo-1: Hi! Let’s start a quiz. Who is this?
First hint: He’s is a Japanese baseballplayer.
Whowhoo-2: Shigeo Nagashima?
Whowhoo-1: No, but Shigeo Nagashima is also aJapanese baseball player.
Second hint: His wife is five years
older than he is.
Whowhoo-2: Hiromitsu Ochiai?
Whowhoo-1: No, but Hiromitsu Ochiai is close to
the correct answer. In fact, this person was also a member of Chunichi
Dragons.
Third hint: His team was defeated by
Yanai High School in the finals at
Koshien high school baseball championship.
Whowhoo-2: Ludwig van Beethoven?
Whowhoo-1: No, Ludwig van Beethoven is far
from the correct answer.
This person was born much later
than Ludwig van Beethoven.
Fourth hint: His name begins with B.
To make the quiz challenging enough, these hints
are presented to the answerer in the order from themost ambiguous and thus most difficult one to themost definitive and thus easiest one based on thequestion answering technology*4.
Just overhearing this conversation allows us to extend
our knowledge about the domain. You can join andanswer the quiz yourself if you want. Our extremely
large vocabulary speech recognition technology*3 [10]enables the recognition of a large vocabulary of two
million words including many personal names. These
entries were extracted from a large collection of documents by using named entity recognition (NER)*6.
Whowhoo indicates how close the answer was to
the right one. It judges inter-person distances by*6 Named entity recognition (NER): We developed
NER systems based on state-of-the-art machinelearning methods. Whowhoo uses a Japanese NERsystem based on a machine learning method called
support vector machines (SVM). Since standard
SVM algorithms are too inefficient to process gigabytes of documents, we devised an efficient algorithm optimized for NER [20]. To extract names
from speech, we implemented a more robust NER

Vol. 4 No. 12 Dec. 2006

using network mining techniques [18], [19], whichanalyze inter-person relationships and visualize theresult as a network on a monitor screen. It is just likelooking into the brain of Whowhoo.
5. Concluding remarks
This paper introduced demonstration systems thatpartially achieve our concept of ambient intelligenceand described the fundamental technologies that wehave developed so far. These technologies for ambient intelligence could transparently permeate oureveryday lives. To create more ideal lifestyles andenhanced personal satisfaction through the use ofambient intelligence, a transdisciplinary approachthat links related technical fields is essential. Whilesome of these technologies have already been used incommercial products, their practical utility must be
further enhanced and new application scenarios mustbe found. New ideas leading to future business should
emerge from the lifestyles created by ambient intelligence.
Ambient Intelligence (kankyo chinou) Symposium 2006—The Future: A Tapestry Wovenfrom Threads of Intelligence
To raise issues about the possibilities, future
visions, and problems of information and communication technology (ICT) with a focus onambient intelligence and to assert the importanceof NTT’s basic research related to ICT, we held
“Ambient Intelligence (kankyo chinou) Symposium 2006—The Future: A Tapestry Woven fromThreads of Intelligence” on September 22, 2006.
The reports are summarized in another article inthis issue [23]. A video of the symposium is available [24] for those interested in the details of the
discussions at the symposium.

system because automatic speech recognition(ASR) systems misrecognize some words [21]. To
make the NER system more accurate, we are also
working on another machine learning method called
conditional random fields (CRF). To get better
results in an arbitrary evaluation measure, we
devised a general “consistent” framework for training CRF [22].

23Selected Papers

References[1]

NTT Communication Science Laboratories (ed.), “The World ofMushrooms,” private edition, http://www.brl.ntt.co.jp/cs/kikang/
index.html (Please contact us if you would like to receive a copy ofthe printed booklet (available in Japanese and English versions)).
[2] E. Maeda and Y. Minami, “Steps towards Ambient Intelligence,”
NTT Technical Review, Vol. 4, No. 1, pp. 50-55, 2006.
[3] E. Maeda, Y. Minami, and K. Dohsaka, “Resurgence of Fairies andGoblins—A Proposal for the New Vision of Ambient Intelligence,”
IPSJ Magazine, Vol. 47, No. 6 , pp. 624-640, 2006 (in Japanese).
[4] K. Ducatel, M. Bogdanowicz, F. Scapolo, J. Leijten, and J-C. Burgelman, “Scenarios for Ambient Intelligence in 2010 Final Report,”
ISTAG, 2001.
[5] P. Remagnino, G. L. Foresti, and T. Ellis (eds.), “Ambient Intelligence: A Novel Paradigm,” Springer, 2004.
[6] G. Riva, F. Vatalaro, F. Davide, and M. Alcaniz (eds.), “AmbientIntelligence: The Evolution of Technology, Communication and Cognition Towards the Future of Human-computer Interaction,” IOS
Press, 2005.
http://www.ambientintelligence.org (2005)[7] NTT Communication Science Laboratories (ed.), “The World ofMushrooms 2006,” private edition (in Japanese). (Please contact us if
you would like to receive a copy of this booklet).
[8] P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” Proc. of CVPR, Vol. 1, pp. 511-518, 2001.
[9] H. Sawada, S. Araki, R. Mukai, and S. Makino, “Blind Extraction of
Dominant Target Sources Using ICA and Time-Frequency Masking,”
IEEE Trans. Audio, Speech, and Language Processing, Vol. 14, No.
6, pp. 2165-2173, Nov. 2006.
[10] A. Nakamura, T. Oba, S. Watanabe, K. Ishizuka, T. Hori, M. Schuster, E. McDermott, and Y. Minami, “A benchmark evaluation of
speech recognizer SOLON using the Corpus of Spontaneous Japanese,” Proc. of Spring ASJ meeting (in Japanese), pp. 1185-1186,
2006.
[11] T. Hori, C. Hori, and Y. Minami, “Fast on-the-fly composition forweighted finite-state transducers in 1.8 million-word vocabulary continuous speech recognition,” Proc. of Interspeech 2004, pp. 289-292,
2004.
[12] H. Isozaki, “An Analysis of a High Performance Japanese Question

24

Answering System,” ACM Transaction on Asian Language Information Processing, Vol. 4, No. 3, pp. 263-279, 2005.
[13] R. Higashinaka, R. Prasad, and M. Walker, “Learning to Generate
Naturalistic Utterances Using Reviews in Spoken Dialogue Systems,”
COLING-ACL-2006, pp. 265-272, 2006.
[14] K. Kashino, T. Kurozumi, and H. Murase, “A Quick Search Method
for Audio and Video Signals Based on Histogram Pruning,” IEEE
Trans. Multimedia, Vol. 5, No. 3, pp. 348-357, 2003.
[15] K. Kashino, A. Kimura, and T. Kurozumi, “A Quick Video Search
Method Based on Local and Global Feature Clustering,” Proc. ofInternational Conference on Pattern Recognition (ICPR), Vol. 3, pp.
894-897, 2004.
[16] T. Kurozumi, K. Kashino and H. Murase, “A Robust Audio Searching Method for Cellular-Phone-Based Music Information Retrieval,”
Proc. of ICPR2002, Vol. 3, pp. 991-994, Aug. 2002.
[17] H. Nagano, K. Kashino and H. Murase, “A Fast Search Algorithm for
Background Music Signals Based on the Search for Numerous Small
Signal Components,” Proc. of the 2003 International Conference on
Acoustics, Speech, and Signal Processing (ICASSP’03), Vol. V, pp.
796-799, Apr. 2003, also in Proc. of 2003 IEEE International Conference on Multimedia and Expo (ICME 2003), Vol. I, pp. 165-168, July
2003.
[18] K. Saito, T. Yamada, and K. Kazama, “Extracting Communities from
Complex Networks by the k-dense Method,” 2nd International Workshop on Mining Complex Data (in press), 2006.
[19] K. Kazama, K. Saito, T. Yamada, and T. Matsubayashi, “Analysis of
Reader Network on a Blog Service,” JWEIN06, 2006 (in Japanese).
[20] H. Isozaki and H. Kazawa, “Efficient Support Vector Classifiers for
Named Entity Recognition,” Proc. of COLING-2002, pp. 390-396,
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[21] K. Sudoh, H. Tsukada, and H. Isozaki, “Incorporating speech recognition confidence into discriminative named entity recognition of
speech data,” Proc. of COLING-ACL-2006, pp. 617-624.
[22] J. Suzuki, E. McDermott, and H. Isozaki, “Training Conditional Random Fields with Multivariate Evaluation Measures,” Proc. of COLING-ACL-2006, pp. 217-224.
[23] K. Dohsaka, Y. Minami, A. Mori, and T. Kondo, “Report on “Ambient Intelligence Symposium 2006—The Future: A Tapestry Wovenfrom Threads of Intelligence,” NTT Technical Review, Vol. 4, No.
12, pp. 64-69, 2006 (this issue).
[24] http://www.kecl.ntt.co.jp/KCS2006/ (in Japanese).

NTT Technical Review

Selected Papers

Eisaku Maeda

Hiroshi Sawada

Senior Research Scientist, Supervisor, Group
Leader of Ambient Intelligence Research Group,Media Information Laboratory, NTT Communication Science Laboratories.
He received the B.E. and M.E degrees in biological science and the Ph.D. degree in mathematical engineering from the University ofTokyo, Tokyo, in 1984, 1986, and 1993, respectively. He joined NTT in 1986. He was a guest
researcher at the University of Cambridge, UK,
in 1996-1997. His research interests are in statistical machine learning, intelligence integration,and bioinformatics. He is a senior member ofIEEE and a member of the Institute of Electronics, Information and Communication Engineers(IEICE) of Japan, the Information Processing
Society of Japan (IPSJ), and the Japanese Societyof Bioinformatics.

Senior Research Scientist, Signal Processing
Research Group, Media Information Laboratory,
NTT Communication Science Laboratories.
He received the B.E., M.E., and Ph.D. degrees
in information science from Kyoto University,
Kyoto, in 1991, 1993, and 2001, respectively. He
has been engaged in research on signal processing, microphone array, and blind source separation. He is a senior member of IEEE and a member of IEICE and ASJ.

Yasuhiro Minami
Senior Research Scientist, Ambient Intelligence Research Group, Media Information Laboratory, NTT Communication Science Laboratories.
He received the B.E., M.E., and Ph.D. degrees
in electrical engineering from Keio University,
Tokyo, in 1986, 1988, and 1991, respectively. He
joined NTT in 1991 and worked on robust speech
recognition. He was a visiting researcher at MIT
from 1999 to 2000. Since February 2000, he has
been with NTT Communication Science Laboratories. He is interested in modeling for speech
recognition. He is a member of IEEE, IEICE,IPSJ, and the Acoustical Society of Japan (ASJ).

Atsushi Nakamura
Senior Research Scientist, Signal Processing
Research Group, Media Information Laboratory,
NTT Communication Science Laboratories.
He received the B.E., M.E., and Dr.Eng.
degrees in communication engineering from
Kyushu University, Fukuoka, in 1985, 1987, and2001, respectively. He joined NTT in 1987. His
research interests include speech recognitionand learning theory and its application to spoken
language processing. He is a member of IEEE,
IEICE, and ASJ.

Junji Yamato
Masato Miyoshi
Senior Research Scientist, Supervisor, Group
Leader of Signal Processing Research Group,Media Information Laboratory, NTT Communication Science Laboratories.
He received the B.E., M.E., and Ph.D. degrees
in electrical engineering from Doshisha University, Kyoto, in 1981, 1983, and 1991, respectively. Since joining Nippon Telegraph and Telephone Public Corporation (now NTT) as a
researcher in 1983, he has continued to study signal processing theory and its application toacoustic technologies. In 1991-1992, he was avisiting researcher at Univ. Bochum, Germany.
He is a senior member of IEEE and a member of
IEICE, ASJ, and the Audio Engineering Society.

Minako Sawaki
Senior Research Scientist, Recognition
Research Group, Media Information Laboratory,
NTT Communication Science Laboratories.
She received the B.E. degree in electrical engineering from Keio University, Tokyo, in 1989.
She joined NTT in 1989. She is interested in pattern recognition and computer vision. She is amember of IEICE and the Institute of Image
Information and Television Engineers.

Senior Research Scientist, Supervisor, Group
Leader of Recognition Research Group, MediaInformation Laboratory, NTT Communication
Science Laboratories.
He received the B.E, M.E., and Ph.D. degrees
in precision machinery engineering from the
University of Tokyo, Tokyo, in 1988, 1990, and2001, respectively, and the S.M. degree in electrical engineering and computer science from
MIT, Massachusetts, in 1998. He joined NTT in
1990. His research interests are in computer
vision, human-computer interaction, and robotics. He is a member of IEEE, the Association for
Computing Machinery (ACM), IEICE, and the
Robotics Society of Japan.

Takeshi Yamada
Senior Research Scientist, Supervisor, Group
Leader of Emergent Learning and Systems
Research Group, Innovative Communication
Laboratory, NTT Communication Science Laboratories.
He received the B.S. degree in mathematics
from the University of Tokyo, Tokyo, in 1988
and the Ph.D. degree in informatics from Kyoto
University, Kyoto, in 2003. He joined NTT in
1988. His research interests are in machine learning, data mining, and combinatorial optimization. He is a member of ACM, IEEE, IEICE,IPSJ, and the Scheduling Society of Japan.

Ryuichiro Higashinaka
Research Scientist, Knowledge Processing
Research Group, Innovative Communication
Laboratory, NTT Communication Science Laboratories.
He received the B.A. degree in environmental
information and the Master of Media and Governance degree from Keio University, Tokyo, in1999 and 2001, respectively. He joined NTT in
2001. During 2004-2006, he was a visiting
researcher at the University of Sheffield, UK. His
research interests are in question answering systems and utterance understanding and generationin spoken dialogue systems. He is a member ofthe Association for Computational Linguistics
and IPSJ.

Vol. 4 No. 12 Dec. 200625