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» A Markov Random Field Model for Automatic Speech Recognition
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ICASSP
2009
IEEE
13 years 5 months ago
Applying discretized articulatory knowledge to dysarthric speech
This paper applies two dynamic Bayes networks that include theoretical and measured kinematic features of the vocal tract, respectively, to the task of labeling phoneme sequences ...
Frank Rudzicz
FGR
2006
IEEE
205views Biometrics» more  FGR 2006»
14 years 1 months ago
Tracking Using Dynamic Programming for Appearance-Based Sign Language Recognition
We present a novel tracking algorithm that uses dynamic programming to determine the path of target objects and that is able to track an arbitrary number of different objects. The...
Philippe Dreuw, Thomas Deselaers, David Rybach, Da...
ECCV
2010
Springer
13 years 11 months ago
Automatic Learning of Background Semantics in Generic Surveilled Scenes
Advanced surveillance systems for behavior recognition in outdoor traffic scenes depend strongly on the particular configuration of the scenario. Scene-independent trajectory analy...
Carles Fernández, Jordi Gonzàlez, Xavier Roca
LREC
2008
174views Education» more  LREC 2008»
13 years 9 months ago
Automatic Phoneme Segmentation with Relaxed Textual Constraints
Speech synthesis by unit selection requires the segmentation of a large single speaker high quality recording. Automatic speech recognition techniques, e.g. Hidden Markov Models (...
Pierre Lanchantin, Andrew C. Morris, Xavier Rodet,...
ICML
2009
IEEE
14 years 8 months ago
Matrix updates for perceptron training of continuous density hidden Markov models
In this paper, we investigate a simple, mistakedriven learning algorithm for discriminative training of continuous density hidden Markov models (CD-HMMs). Most CD-HMMs for automat...
Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul