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IJON
2006
90views more  IJON 2006»
15 years 6 months ago
Reinforcement learning of a simple control task using the spike response model
In this work, we propose a variation of a direct reinforcement learning algorithm, suitable for usage with spiking neurons based on the spike response model (SRM). The SRM is a bi...
Murilo Saraiva de Queiroz, Roberto Coelho de Berr&...
SPEECH
2008
124views more  SPEECH 2008»
15 years 6 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
TNN
1998
146views more  TNN 1998»
15 years 6 months ago
Fuzzy lattice neural network (FLNN): a hybrid model for learning
— This paper proposes two hierarchical schemes for learning, one for clustering and the other for classification problems. Both schemes can be implemented on a fuzzy lattice neu...
Vassilios Petridis, Vassilis G. Kaburlasos
PAMI
2007
121views more  PAMI 2007»
15 years 6 months ago
Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation
— In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is uniq...
Sébastien Marcel, José del R. Mill&a...
ICMCS
2009
IEEE
189views Multimedia» more  ICMCS 2009»
15 years 4 months ago
Emotion recognition from speech VIA boosted Gaussian mixture models
Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, ...
Hao Tang, Stephen M. Chu, Mark Hasegawa-Johnson, T...