Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition

Pinto, Joel

doi:10.5075/epfl-thesis-4649

2010

DOI: 10.5075/epfl-thesis-4649

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Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition

Joel Pinto¹

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Cited by 2 publications

References 111 publications

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A Learning Process of Multilayer Perceptron for Speech Recognition

Azzizi¹,

Zaatri²

2016

Int. J. of Pure and Appl. Math.

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For learning artificial systems as well as for living systems, it is generally proven that the learning performances improve with the experience. This paper seeks to analyze the learning process of an artificial system: a Multi-Layer Perceptron Neural Nets (MLP-NN) used for word recognition and dedicated for robot control. As the MLP requires references for the spoken words, we have provided these references by means of a supervised classifier based on minimizing the mean square error.We are particularly interested by estimating the minimal number of trials required to ensure the recognition of some spoken words by the MLP-NN with an acceptable predefined error. To this purpose, we have experimentally performed the learning process of the recognition of some specific words. For each word, we have recorded the performance improvement with respect to the number of trials enabling to draw the learning curve. The mathematical modeling of these curves presents a bi-exponential law profile while the mathematical modeling of human performance show generally a power law profile. The obtained results have led to a better understanding of the artificial system performance under the influence of internal

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A Learning Process of Multilayer Perceptron for Speech Recognition

Azzizi¹,

Zaatri²

2016

Int. J. of Pure and Appl. Math.

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Neural Network for Numeral Recognition

Benchaou¹,

Nasri²,

Melhaoui³

2015

IJCA

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The present paper proposes a new approach of preprocessing for handwritten, printed and isolated numeral characters. The new approach reduces the size of the input image of each numeral by discarding the redundant information. This method reduces also the number of features of the attribute vector provided by the extraction features method. Numeral recognition is carried out in this work through k nearest neighbors and multilayer perceptron techniques. The simulations have obtained a good rate of recognition in fewer running time. General Terms Pattern Recognition, image processing, feature extraction, neural network. Keywords Handwritten and printed numeral recognition, preprocessing, profile projection, k nearest neighbors, multilayer perceptron.

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Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition

Cited by 2 publications

References 111 publications

A Learning Process of Multilayer Perceptron for Speech Recognition

A Learning Process of Multilayer Perceptron for Speech Recognition

Neural Network for Numeral Recognition

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