We have released MMDAgent-EX
We have released MMDAgent-EX, our open-source platform for CG avatar based spoken dialogue system, multimodal dialogue and avatar communication.
Links:
Press release (by NITech, in Japanese) Official site GitHub
Information
"Uka" has been added to the CG AGENTS page.
A third CG dialogue agent “Uka” has been added to the CG AGENTS page.
"CG AGENTS" page is now available.
A new “CG AGENTS” page has been added to the home page.
Assistant Professor Sei Ueno has joined Lee Lab.
Assistant Professor Sei Ueno joined Lee lab in April. Together with Assist. Prof. Ueno, we will continue our research on spoken language information processing and spoken dialogue interfaces.
Joined CAO/JST Moonshot Project
From December 2020, Our lab has been participating in the “Avatar Symbiotic Society” project, a moonshot-type research and development project led by Professor Ishiguro of Osaka University.
“CG-Specific Dialogue” is our research theme. We are focusing on a new conversation system with CG characters that seamlessly integrates an autonomous dialogue system and human remote control (avatars) to realize truly usable, rich-enpowered human-communication system on the next era. We are now working in collaboration with various research institutes.
Julius-4.6 Released
Julius version 4.6 has been released. You can get it from its GitHub site.
What’s new in Julius-4.6 Julius-4.6 is a minor release with new features and fixes, including GPU integration and grammar handling updates.
GPU-based DNN-HMM computation (Take a look at v4.6 performance comparison on YouTube!)
Now Julius can compute DNN-HMM with GPU. Total decoding will be four times faster than CPU-based computation on Julius-4.5.
Requires CUDA version 8, 9 or 10.
Web site renewal
The Lab’s web site has been re-organized with Hugo with refreshed English page. Goodbye WordPress!
Julius: added a new feature
Julius has merged a pull request that adds a new feature “grammar search on the 1st pass”. To use it, get the latest code on master branch.
It enables applying full grammar on the 1-pass, thus outputs more reliable (grammar-constrained) result at the 1st pass.
Background The grammar-based recognition on Julius does not apply the full grammar on the 1st pass, but applies only the word-pair constraint extracted from the grammar for efficiency.