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The IBM 2006 Spoken Term Detection System

The IBM 2006 Spoken Term Detection System

The IBM 2006 Spoken Term Detection System Jonathan Mamou IBM Haifa Research Labs Olivier Siohan Bhuvana Ramabhadran IBM T. J. Watson Research Center Outline System description Indexing

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693 views • 27 slides
Advances of AND/OR-tree Search Algorithms in Shogi Mating Search

Advances of AND/OR-tree Search Algorithms in Shogi Mating Search

Advances of AND/OR-tree Search Algorithms in Shogi Mating Search. Hiroyuki Iida?and Makoto Sakuta Department of Computer Science Shizuoka University, Japan. The 6 th Computer Olympiad: Computer-Games Workshop (Maastricht, 2001). Overview.

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402 views • 27 slides
Cold Fusion Verity Oracle Enterprise Search Acrobat PDF Index Assistant Comparison

Cold Fusion Verity Oracle Enterprise Search Acrobat PDF Index Assistant Comparison

Cold Fusion Verity Oracle Enterprise Search Acrobat PDF Index Assistant Comparison. Arden O. Weiss ardenweiss@ verizon .net. Scope of Presentation:. The Application using these Tools The Menu Structure Tool Pros/Cons Output Examples Conclusions. The Application Purposes:.

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520 views • 24 slides
Audio Fingerprinting

Audio Fingerprinting

Audio Fingerprinting. Wes Hatch MUMT-614 Mar.13, 2003. What is Audio Fingerprinting?. a small, unknown segment of audio data (it can be as short as just a couple of seconds) is used to identify the original audio file from which it came. Applications. Broadcast monitoring

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378 views • 17 slides
What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

dim = argmax dim [var(pts dim )] split = median(pts dim ) L_pts = [pts dim < split] R_pts = [pts dim > split]. PCA Tree. axis = pca(pts).eigvec[0] curpts = project(pts, axis) split = median(curpts) L_pts = [curpts < split] R_pts = [curpts > split]. Ball Tree.

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175 views • 1 slides
What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

dim = argmax dim [var(pts dim )] split = median(pts dim ) L_pts = [pts dim < split] R_pts = [pts dim > split]. PCA Tree. axis = pca(pts).eigvec[0] curpts = project(pts, axis) split = median(curpts) L_pts = [curpts < split] R_pts = [curpts > split]. Ball Tree.

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117 views • 1 slides

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