Combining the results of several neural network classifiers
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear time series analysis
Nonlinear time series analysis
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Speech Communication
Hybrid statistical pronunciation models designed to be trained by a medium-size corpus
Computer Speech and Language
Time Series Classification Using Gaussian Mixture Models of Reconstructed Phase Spaces
IEEE Transactions on Knowledge and Data Engineering
Likelihood-maximizing-based multiband spectral subtraction for robust speech recognition
EURASIP Journal on Advances in Signal Processing
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Statistical models of reconstructed phase spaces for signal classification
IEEE Transactions on Signal Processing - Part I
Time---domain non-linear feature parameter for consonant classification
International Journal of Speech Technology
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Investigating new effective feature extraction methods applied to the speech signal is an important approach to improve the performance of automatic speech recognition (ASR) systems. Owing to the fact that the reconstructed phase space (RPS) is a proper field for true detection of signal dynamics, in this paper we propose a new method for feature extraction from the trajectory of the speech signal in the RPS. This method is based upon modeling the speech trajectory using the multivariate autoregressive (MVAR) method. Moreover, in the following, we benefit from linear discriminant analysis (LDA) for dimension reduction. The LDA technique is utilized to simultaneously decorrelate and reduce the dimension of the final feature set. Experimental results show that the MVAR of order 6 is appropriate for modeling the trajectory of speech signals in the RPS. In this study recognition experiments are conducted with an HMM-based continuous speech recognition system and a naive Bayes isolated phoneme classifier on the Persian FARSDAT and American English TIMIT corpora to compare the proposed features to some older RPS-based and traditional spectral-based MFCC features.