Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Speech recognition by machines and humans
Speech Communication
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Efficient cepstral normalization for robust speech recognition
HLT '93 Proceedings of the workshop on Human Language Technology
Sphinx-4: a flexible open source framework for speech recognition
Sphinx-4: a flexible open source framework for speech recognition
Discrimination of speech from nonspeech based on multiscale spectro-temporal Modulations
IEEE Transactions on Audio, Speech, and Language Processing
Evolutionary optimization of a hierarchical object recognition model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hierarchical framework for spectro-temporal feature extraction
Speech Communication
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In this paper we propose a feedforward neural network for syllable recognition. The core of the recognition system is based on a hierarchical architecture initially developed for visual object recognition. We show that, given the similarities between the primary auditory and visual cortexes, such a system can successfully be used for speech recognition. Syllables are used as basic units for the recognition. Their spectrograms, computed using a Gammatone filterbank, are interpreted as images and subsequently feed into the neural network after a preprocessing step that enhances the formant frequencies and normalizes the length of the syllables. The performance of our system has been analyzed on the recognition of 25 different monosyllabic words. The parameters of the architecture have been optimized using an evolutionary strategy. Compared to the Sphinx-4 speech recognition system, our system achieves better robustness and generalization capabilities in noisy conditions.