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This presentation by Konsta Koppinen from Tampere University of Technology explores the application of evolutionary computation methods to enhance speech recognition systems. It covers key topics such as labeled speech feature pooling, including mel-band energy and cepstral coefficients, alongside the use of sparse neural networks for phonetic target neurons. The training process employs evolutionary algorithms for mutation, weight modification, and network complexity management, evaluated through frame-level phonetic targets, ensuring high performance in speech recognition tasks.
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Learning Feature Mappings Using Evolutionary Computation Konsta Koppinen Tampere University of Technology Tampere, Finland ICSI Speech Lunch 1/27/04
Overview l v s Labeled speech ey ax-h Feature pool: -mel-band energy -cepstral coeff -harmonicity -… Sparse neural network Phonetic target neurons b d e f ey a jh s z
Neural Network Training • Training using evolutionary algorithms • mutation • change weight • add/remove neuron • add/remove connection • crossover • Evaluation using frame-level phonetic targets • estimation of performance using sampling • penalty for complexity of network 0.2 0.7 0.1 0.1
