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ICASSP
2009
IEEE
14 years 2 months ago
Combining mixture weight pruning and quantization for small-footprint speech recognition
Semi-continuous acoustic models, where the output distributions for all Hidden Markov Model states share a common codebook of Gaussian density functions, are a well-known and prov...
David Huggins-Daines, Alexander I. Rudnicky
SPEECH
2008
124views more  SPEECH 2008»
13 years 7 months ago
Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model
In this paper, we describe a statistical approach to both an articulatory-to-acoustic mapping and an acoustic-to-articulatory inversion mapping without using phonetic information....
Tomoki Toda, Alan W. Black, Keiichi Tokuda
ICPR
2004
IEEE
14 years 8 months ago
Bayesian Face Recognition Based on Gaussian Mixture Models
Bayesian analysis is a popular subspace based face recognition method. It casts the face recognition task into a binary classification problem with each of the two classes, intrap...
Xiaogang Wang, Xiaoou Tang
ICASSP
2008
IEEE
14 years 1 months ago
Analysis-by-synthesis features for speech recognition
We present a framework for speech recognition that accounts for hidden articulatory information. We model the articulatory space using a codebook of articulatory configurations g...
Ziad Al Bawab, Bhiksha Raj, Richard M. Stern
CVPR
2005
IEEE
14 years 9 months ago
Subspace Analysis Using Random Mixture Models
In [1], three popular subspace face recognition methods, PCA, Bayes, and LDA were analyzed under the same framework and an unified subspace analysis was proposed. However, since t...
Xiaogang Wang, Xiaoou Tang