Associate Professor Shimodaira joined JAIST in April, 1992 from Tohoku
University. He received his Ph.D. in 1988 from Tohoku University and
from 1988 to 1991 he was a Research Associate of Faculty of
Information Science, Tohoku University. He was a visitor at ATR
Interpreting Telephony Research Laboratories in the summer of 1990.
From 1995 to 1996, he was a visiting researcher at CSTR,
The University of Edinburgh.
"Recognition of Devanagari Characters Using Neural Networks",
IEICE Trans. Inf. & Syst., Vol.E79-D, No.5, pp.523-528 (1996-05)
``Automatic Prosodic Segmentation by F0 Clustering Using Superpositional Modeling'',
Proc. of ICASSP-95, pp.624-627, 1995.
``Prosodic Phrase Segmentation by Pitch Pattern Clustering'',
Proc. of ICASSP-94, 76.5, pp.217-220, 1994.
``A Fast VQ Codebook Design Algorithm for a Large Number of Data'',
Proc. of ICASSP-92, I-109, 1992.
``Speaker-Independent Vowel-Sequence Recognition
Based on a Binary Relational Model of Vowels'',
Trans. of IEICE, J71-A, pp.1515-1522, 1988-08 (in Japanese).
Research Project Available
Speaker-independent speech recognition and speech understanding
Handwritten Chinese character recognition
Man-machine interface design
We are currently working in the field of pattern recognition,
especially speech recognition / understanding and character
There are four research subjects I have been concerned with in order
to develop a novel speech understanding system (SUS). One is to solve
the between-speaker variability problem which is one of the most
important, and still very difficult, obstacles for realizing a
speaker-independent SUS. Our research group is trying to model
speaker variations both in static and dynamic domains, i.e. spectral
and temporal domains. The second one is to develop a technique of
incorporating prosodic features into SUS. Prosodic features, such as
accent, intonation and pauses, are noticed as super segmental features
of spoken language, and provide important information for
comprehending speech. Although these features have not been used
successfully in SUS because of their unreliable and unstable
properties, we expect that stochastic analysis of the prosodic
features and optimal search algorithms will enable us to make use of
the features for SUS. The two remaining subjects are to study
man-machine interface, and to develop efficient algorithms for
optimization problems useful to pattern recognition.
Automatic recognition of handwritten Chinese characters still remains
a very difficult task in spite of intensive research over 10 years.
One of the main reasons is the various differences in character images
drawn by different writers. We have been studying several kinds of
pattern transformation methods to overcome this obstacle. In
addition, we have been developing an easy-to-use character-to-braille
translation system for blind readers.