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NN
2006
Springer
13 years 7 months ago
Propagation and control of stochastic signals through universal learning networks
The way of propagating and control of stochastic signals through Universal Learning Networks (ULNs) and its applications are proposed. ULNs have been already developed to form a s...
Kotaro Hirasawa, Shingo Mabu, Jinglu Hu
ICASSP
2011
IEEE
12 years 11 months ago
Arccosine kernels: Acoustic modeling with infinite neural networks
Neural networks are a useful alternative to Gaussian mixture models for acoustic modeling; however, training multilayer networks involves a difficult, nonconvex optimization that...
Chih-Chieh Cheng, Brian Kingsbury
ICASSP
2009
IEEE
13 years 11 months ago
Neural network based language models for highly inflective languages
Speech recognition of inflectional and morphologically rich languages like Czech is currently quite a challenging task, because simple n-gram techniques are unable to capture impo...
Tomas Mikolov, Jirí Kopecký, Lukas B...
ICASSP
2011
IEEE
12 years 11 months ago
Extensions of recurrent neural network language model
We present several modifications of the original recurrent neural network language model (RNN LM). While this model has been shown to significantly outperform many competitive l...
Tomas Mikolov, Stefan Kombrink, Lukas Burget, Jan ...
IROS
2008
IEEE
161views Robotics» more  IROS 2008»
14 years 1 months ago
Segmenting acoustic signal with articulatory movement using Recurrent Neural Network for phoneme acquisition
— This paper proposes a computational model for phoneme acquisition by infants. Human infants perceive speech sounds not as discrete phoneme sequences but as continuous acoustic ...
Hisashi Kanda, Tetsuya Ogata, Kazunori Komatani, H...