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  • Upgrade business through AI Emeishan, Sichuan province, China, CN-EN, August 24, 2017 - Fujitsu R&D Center Co., Ltd. (FRDC) successfully hosted its annual Chinese Technology Forum (FRDC-CTF 2017) at Hongzhushan Hotel in Emeishan, Sichuan Province, China.


  • Fujitsu Develops Automatic DNN Construction System Beijing,China, CN-EN, July 19, 2017 - Fujitsu R&D Center Co., Ltd. and Fujitsu Laboratories Ltd. (collectively "Fujitsu"). today announced an advanced deep learning technology for helping non-professional users to employ DNN models to solve their problems. By using this system, well-balanced DNN models between the accuracy and time consumption will be automatically generated based on the given task of the user. Furthermore, the system is able to provide the DNN model to the user within couple of hours while the conventional process may take several days. We hope this technology can make further contribution to enlarge the DNN application field and realize the general AI platform.


  • Deep Learning-based Voiceprint Authentication from Very Short Speeches Beijing, China, CN-EN, March 09, 2017 - Fujitsu R&D Center Co., Ltd. (FRDC) announced the development of a kind of high-precision voiceprint authentication technology, and this technology, by making use of deep learning approach, is able to identify the speaker from a very short speech segment. This technology combines two deep learning engines, one is used to extract speech content-related features, and the other speaker-related features, thus realizing the “voice password” identity authentication functions, that is: the identity of the speaker can be accepted only when the speaker himself/herself speaks out correctly the preset contents. With this technology, the error rate of identity authentication is about 2.2% with a short speech of less than 3 seconds.


  • Deep learning with a Reduced Number of Training Data Beijing, China, CN-EN, February 21, 2017 - today announced an advanced deep learning technique for Chinese ancient character recognition.By applying the developed technique, highly accurate character recognition can be achieved with only a small number of training data.