Doshisha University (Private)

similar items with the searched item, pattern recognition in other words. Please say “good morning” out loud. Everyone says this in a
different manner with a different voice. By no means do different people speak with exactly the same voice pattern. Even a single
person’s voice will be different each time they say “good morning.” We humans hear this differing pattern as the same words, “good
morning,” without any problem. But to make a computer listen like this is not easy at all.
We in the Co-Creation Informatics Laboratory aim to advance these pattern recognition technologies by researching and developing
new recognition system design methods with the cutting edge technique called the minimum classification error training method (or
the generalized probabilistic descent method) as the foundation. The basic concept is simple. The basis of recognition is in
comparisons. The “good morning” pattern the computer is trying to recognize is compared with a number of patterns stored on a
computer .If the stored “good morning” pattern is clearly more similar to the “good morning” pattern to be recognized rather than
other patterns like “good evening,” there are no problems. The pattern is correctly recognized. However, let’s make the stored “good
morning” pattern that of an adult male. And then let’s make the stored pattern for “good evening” a child’s voice. At this time, if the
“good morning” to be recognized is a child’s voice, this “good morning” may be judged more similar to the child’s “good evening”
rather than the adult’s “good morning.” Depending on whether the computer judges the similarity in voices or words, we can
understand that these kinds of variations or errors can occur as a result. In order to prevent these kinds of errors, our technique is to
repeat changes in the stored “good morning” and “good evening” patterns to achieve accurate recognition, or learning in other words.
We ourselves have been involved in the development of the minimum classification error training method. With this background, we
are advancing research to further improve and develop minimum classification error training while competing at an international
standard.
Keywords
Remote Communication and Collaboration
t-Room
Multi-media Signal Processing
Pattern Recognition
Discriminative Training
Minimum Classification Error
Generalized Probabilistic Descent
Data Mining
Knowledge Discovery
Clinical Data