Jeff A. Bilmes: Software and Data

Software

• The graphical models toolkit GMTK
• The Vocal Joystick Software for windows.
• The PhiPAC automatically tuning matrix-matrix multiply library (and the first auto-tuning matrix multiply dense linear algebra library).
• The Measure propagation code and web page, a graph Laplacian Manifold approximation based semi-supervised learning algorithm that uses an objective function based on KL-divergence (code also includes fast C++ parallel implementation).
• The Buried Markov Model (BMM) code (includes mixtures of sparse linear conditional multi-time Gaussian models). Sorry, no documentation.
• Multi-party meeting scheduling with simple preference aggregation rules.
• Extensions to the old Berkeley parallel make software (or what is known as pmake). The original pmake utility is described here. We have made a number of significant extensions to pmake including a full gnu autoconf configuration, many new resource constraints (including dynamic), and other features (as well as removed some old ones that were no longer needed). The complete source code is at at pmake-3.0-alpha. Note that this is an alpha release, and is basically working but there are no plans for additional work to be done on this (at least by me or my group), nor can I answer any further questions about this (see the source code).

Data

• Cooperative Cut image data , a set of difficult to segment images (with elongated or narrow structures, and contrast gradients) along with ground truth labellings, and that were used in the following paper
• Vocal Joystick Vowel Corpus
• A small amount of hand-aligned French/English data, useful for statistical machine translation systems, done by Karim Filali.
• The COSINE multi-channel real-world in-situ noisy speech corpus.
(now available for download).
• The Semi-Supervised Switchboard Transcription (S3TP) project and its data. In the 1990s, the switchboard transcription project gave us 1.5 hours of frame-by-frame phonetically transcribed switchboard conversational speech data. Here, we have used a modern semi-supervised learning algorithm to phonetically label at the frame level the remaining 250 hours of SWB-I, and we call this the semi-supervised switchboard transcription project (or s3tp). The data and algorithms are available here.