the speech is an easily accessible signal which clearly represents the characteristics of larynx and vocal folds. therefore, application of some proper machine learning algorithms on a small part of a recorded speech signal may help in non-invasive diagnosing of vocal fold diseases. since there are some experimental evidences that suggest the existence of chaotic behavior in speech production s...

this paper introduces a novel approach to improve performance of speech recognition systems using a combination of features obtained from speech reconstructed phase space (rps) and frequency domain analysis. by choosing an appropriate value for the dimension, reconstructed phase space is assured to be topologically equivalent to the dynamics of the speech production system, and could therefore ...

This paper presents a novel method for speech recognition by utilizing nonlinear/chaotic signal processing techniques to extract time-domain based phase space features. By exploiting the theoretical results derived in nonlinear dynamics, a processing space called a reconstructed phase space can be generated where a salient model (the natural distribution of the attractor) can be extracted for s...

Although isolated phoneme classification using features from time-domain phase space reconstruction has been investigated recently, the best representation of feature vectors for the discriminability over phoneme classes is still an open question. This paper applies Principal Component Analysis (PCA) to feature vectors from the reconstructed phase space. By using PCA projection, the basis of th...

Background: Epilepsy is a brain disorder that changes the basin geometry of the oscillation of trajectories in the phase space. Nevertheless, recent studies on epilepsy often used the statistical characteristics of this space to diagnose epileptic seizures. Objectives: We evaluated changes caused by the seizures on the mentioned basin by focusing on phase space sorted by Poincaré sections. Ma...

A novel method for speech recognition is presented, utilizing nonlinear/chaotic signal processing techniques to extract timedomain based, reconstructed phase space derived features. By exploiting the theoretical results derived in nonlinear dynamics, a distinct signal processing space called a reconstructed phase space can be generated where salient features (the natural distribution and trajec...

Although isolated phoneme classification using features from time-domain phase space reconstruction has been investigated recently, the best representation of feature vectors for the discriminability over phoneme classes is still an open question. This paper applies Principal Component Analysis (PCA) to feature vectors from the reconstructed phase space. By using PCA projection, the basis of th...