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» Sparseness Achievement in Hidden Markov Models
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NIPS
2008
13 years 9 months ago
Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction
We explore a new Bayesian model for probabilistic grammars, a family of distributions over discrete structures that includes hidden Markov models and probabilistic context-free gr...
Shay B. Cohen, Kevin Gimpel, Noah A. Smith
INTERSPEECH
2010
13 years 2 months ago
Synthesis of fast speech with interpolation of adapted HSMMs and its evaluation by blind and sighted listeners
In this paper we evaluate a method for generating synthetic speech at high speaking rates based on the interpolation of hidden semi-Markov models (HSMMs) trained on speech data re...
Michael Pucher, Dietmar Schabus, Junichi Yamagishi
ICIP
2002
IEEE
14 years 9 months ago
A study of contextual modeling and texture characterization for multiscale Bayesian segmentation
In this paper, we demonstrate that multiscale Bayesian image segmentation can be enhanced by improving both contextual modeling and statistical texture characterization. Firstly, ...
Guoliang Fan, Xiaomu Song
ICGI
1994
Springer
13 years 11 months ago
Inducing Probabilistic Grammars by Bayesian Model Merging
We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are incorporated by adding ad-hoc rules to a working grammar; subseque...
Andreas Stolcke, Stephen M. Omohundro
ICASSP
2010
IEEE
13 years 7 months ago
Automatic state discovery for unstructured audio scene classification
In this paper we present a novel scheme for unstructured audio scene classification that possesses three highly desirable and powerful features: autonomy, scalability, and robust...
Julian Ramos, Sajid M. Siddiqi, Artur Dubrawski, G...