Accelerating backpropagation through dynamic self-adaptation
Neural Networks
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Gaussian sum approach with optimal experiment design for neural network
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Using Morphological Information for Robust Language Modeling in Czech ASR System
IEEE Transactions on Audio, Speech, and Language Processing
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This paper describes ANN based posterior estimates and their application to speech recognition. We replaced the standard back-propagation with the L-BFGS quasi-Newton method. We have focused only on posterior based feature vector extraction. Our goal was a feature vector dimension reduction. Thus we designed three posterior transforms to space with dimensionality 1 or 2. The designed transforms were tested on the SpeechDat-East corpus. We also applied the introduced method on a Czech audio-visual corpus. In both cases the methods leads to significant word error rate decrease.