3 years ago

Doshisha University (Private)

Doshisha University (Private)

similar items with the

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

Time-series Data Prof. Masashi OKUBO, Takao TSUCHIYA Applied Media Information Laboratory Research Topics Human interface group Research on internal motivation by presenting self-behavior Research on communication support by presenting many kinds of information Research on Kansei shape evaluation Research on estimating emotional stress using various sensors Acoustic group Developing elemental technologies for sound field rendering Researching high-speed sound field rendering by GPU Researching real-time sound field rendering by FPGA (silicon concert hall) Research on Lake Biwa’s water temperature monitoring by acoustic tomography Research on numerical simulations of thermoacoustic phenomena Research Contents 1. In the human interface group, we are researching ways to assist the engagement of people with people, and people with systems. For example, research and development of systems to support communication between people, and research and development of systems that interactively present a person’s movements, exercise, and those results to themselves to encourage self-development based on internal motivation. In addition, in the emotional stress measurement field, which has used contact sensors, we are conducting research on estimating emotional stress using non-contact sensors. We are also researching shape evaluation based on Kansei such as the beauty and complexity of objects in real and virtual spaces.

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