Theory of cellular automata: a survey
Theoretical Computer Science
Auditory cortical representations of speech signals for phoneme classification
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
High-pitch formant estimation by exploiting temporal change of pitch
IEEE Transactions on Audio, Speech, and Language Processing
Discrimination of speech from nonspeech based on multiscale spectro-temporal Modulations
IEEE Transactions on Audio, Speech, and Language Processing
Speech Analysis in a Model of the Central Auditory System
IEEE Transactions on Audio, Speech, and Language Processing
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
IEEE Transactions on Evolutionary Computation
Genetic learning automata for function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Auditory representations of acoustic signals
IEEE Transactions on Information Theory - Part 2
A compressed domain scheme for classifying block edge patterns
IEEE Transactions on Image Processing
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Recently, there has been a significant increase in studies employing auditory models in speech recognition systems. In this paper, we propose a new evolutionary tuned feature extraction method by spectro-temporal analysis. In our proposed model, there is a special subspace for each phoneme with a specific best scale in the spectral filter and a specific best rate in the temporal filter. These two parameters were obtained by genetic cellular automata evolutionary algorithm. The extracted features from the specific subspace are classified by a binary one-versus-rest support vector machine. Finally, a multiclass classifier for all phonemes is employed by combining these sub-models. The proposed method improved the discrimination of phonemes significantly especially in highly confusable phonemes. To show the efficiency of the proposed feature sets, it was empirically compared with two baseline models. The achieved relative improvements are about 10% in classification rate for voiced plosives, unvoiced plosives and nasals; and about 7.38% for front vowels relative to the state of the art baseline model.