Multilayer feedforward networks are universal approximators
Neural Networks
Review of neural networks for speech recognition
Neural Computation
Speech recognition using neural networks
Speech recognition using neural networks
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Connected Letter Recognition with a Multi-State Time Delay Neural Network
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Research on Chinese Digit Speech Recognition Based on Multi-Weighted Neural Network
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 01
Enhanced phone posteriors for improving speech recognition systems
IEEE Transactions on Audio, Speech, and Language Processing
SWAT: a spiking neural network training algorithm for classification problems
IEEE Transactions on Neural Networks
International Journal of Speech Technology
International Journal of Speech Technology
Analysis of MLP-Based Hierarchical Phoneme Posterior Probability Estimator
IEEE Transactions on Audio, Speech, and Language Processing
Noise-Robust Automatic Speech Recognition Using a Predictive Echo State Network
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Transcribing Mandarin Broadcast Speech Using Multi-Layer Perceptron Acoustic Features
IEEE Transactions on Audio, Speech, and Language Processing
Deep and Wide: Multiple Layers in Automatic Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Sparse Multilayer Perceptron for Phoneme Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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This paper introduces a neural network optimization procedure allowing the generation of multilayer perceptron (MLP) network topologies with few connections, low complexity and high classification performance for phoneme's recognition. An efficient constructive algorithm with incremental training using a new proposed Frame by Frame Neural Networks (FFNN) classification approach for automatic phoneme recognition is thus proposed. It is based on a novel recruiting hidden neuron's procedure for a single hidden-layer. After an initializing phase started with initial small number of hidden neurons, this algorithm allows the Neural Networks (NNs) to adjust automatically its parameters during the training phase. The modular FFNN classification method is then constructed and tested to recognize 5 broad phonetic classes extracted from the TIMIT database. In order to take into account the speech variability related to the coarticulation effect, a Context Window of Three Successive Frame's (CWTSF) analysis is applied. Although, an important reduction of the computational training time is observed, this technique penalized the overall Phone Recognition Rate (PRR) and increased the complexity of the recognition system. To alleviate these limitations, two feature dimensionality reduction techniques respectively based on Principal Component Analysis (PCA) and Self Organizing Maps (SOM) are investigated. It is observed an important improvement in the performance of the recognition system when the PCA technique is applied.Optimal neuronal phone recognition architecture is finally derived according to the following criteria: best PRR, minimum computational training time and complexity of the BPNN architecture.