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Statistical Language Modeling Toolkit Jun 7, 1999 ... The CMU-Cambridge Statistical Language Modeling toolkit is a suite of UNIX software tools to facilitate the construction and testing of ... 2.
The use of recurrent neural networks in continuous speech recognition Jun 4, 1995 ... The use of recurrent neural networks in continuous speech recognition. Tony Robinson, Mike Hochberg and Steve Renals ... 3.
Recurrent Networks for Phone Probability Estimation One approach is to consider the forward equations of a standard HMM as recurrent network-like computation. The HMM can then be trained using the maximum ... 4.
The use of recurrent neural networks in continuous speech recognition Jun 4, 1995 ... The use of recurrent neural networks in continuous speech recognition. Tony Robinson, Mike Hochberg and Steve Renals ... 5.
CV - Do Yeong Kim Feb 27, 2006 ... D.Y. Kim, M.J.F. Gales and P.C. Woodland, "Recent Developments at Cambridge in Broadcast News Transcription," MIL Speech Seminar, ... 6.
Infinite Impulse Response filters An Infinite Impulse Response (IIR) filter produces an output, y(n), that is the weighted sum of the current and past inputs, x(n), and past outputs. ... 7.
Univ. of Cambridge, Dept. of Engineering. - Speech Vision and ... Home page of the Speech Vision and Robotics Group, University of Cambridge Department of Engineering. 8.
Computer Vision & Robotics --- Visual Tracking --- Jan 20, 2000 ... Computer Vision and Robotics ... The MPEG videos below show a real-time tracking system for complex 3-dimensional structures: ... 9.
Windowing Rectangular window gives maximum sharpness but large side-lobes (ripples) - hamming window blurs in frequency but produces much less leakage. For example: ... 10.
Intensity-loudness power law Intensity-loudness power law. Perceived loudness, tex2html_wrap_inline3183 , is approximately the cube root of the intensity, tex2html_wrap_inline3185 ... |
