Since the impact of the recent rapid penetration of Information Technologies into the society is so tremendous, it is said that IT revolution is coming. Recognizing the above new waves, the Japanese Government is now promoting e-Government programs, and most enterprises are going to depend on the Internet to do their various activities. However, computer criminals, and other threats to security are increasing and becoming serious. Therefore, 'security' is the key for the Internet to be infrastructure of the future society in a true sense. There are many products for security controls, which are not necessarily compatible or interoperable. Interoperability is the basic requirement for infrastructures. In April, 2000, JNSA was organized by about a hundred IT companies. On the other hand, in October, 2000, LINCS was set up in Kogakuin University. The two organizations set up a Consortium to make experimental studies on IPSec interoperability. This is the first report of the activities and intermediate (the first) results obtained.

We attempted to estimate subjective scores of the Japanese Diagnostic Rhyme Test (DRT), a two-to-one forced selection speech intelligibility test. We used automatic speech recognizers with language models that force one of the words in the word-pair, mimicking the human recognition process of the DRT. Initial testing was done using speaker-independent models, and they showed significantly lower scores than subjective scores. The acoustic models were then adapted to each of the speakers in the corpus, and then adapted to noise at a specified SNR. Three different types of noise were tested: white noise, multi-talker (babble) noise, and pseudo-speech noise. The match between subjective and estimated scores improved significantly with noise-adapted models compared to speaker-independent models and the speaker-adapted models, when the adapted noise level and the tested level match. However, when SNR conditions do not match, the recognition scores degraded especially when tested SNR conditions were higher than the adapted noise level. Accordingly, we adapted the models to mixed levels of noise, i.e., multi-condition training. The adapted models now showed relatively high intelligibility matching subjective intelligibility performance over all levels of noise. The correlation between subjective and estimated intelligibility scores increased to 0.94 with multi-talker noise, 0.93 with white noise, and 0.89 with pseudo-speech noise, while the root mean square error (RMSE) reduced from more than 40 to 13.10, 13.05 and 16.06, respectively.