Automatic Selection of Acoustic and Non-Linear Dynamic Features in Voice Signals for Hypernasality Detection
نویسندگان
چکیده
Automatic detection of hypernasality in voices of children with Cleft Lip and Palate (CLP) is made considering two charcaterization techniques, one based on acoustic, noise and cepstral analysis and other based on nonlinear dynamic features. Besides characterization, two automatic feature selection techniques are implemented in order to find optimal sub-spaces to better discriminate between healthy and hypernasal voices. Results indicate that nonlinear dynamic features are valuable tool for automatic detection of hypernasality; addtionally both feature selection techniques show stable and consistent results, achieving accuracy levels of up to 93.73%.
منابع مشابه
Epileptic seizure detection based on The Limited Penetrable visibility graph algorithm and graph properties
Introduction: Epileptic seizure detection is a key step for both researchers and epilepsy specialists for epilepsy assessment due to the non-stationariness and chaos in the electroencephalogram (EEG) signals. Current research is directed toward the development of an efficient method for epilepsy or seizure detection based the limited penetrable visibility graph (LPVG) algorith...
متن کاملDimensionality Reduction and Improving the Performance of Automatic Modulation Classification using Genetic Programming (RESEARCH NOTE)
This paper shows how we can make advantage of using genetic programming in selection of suitable features for automatic modulation recognition. Automatic modulation recognition is one of the essential components of modern receivers. In this regard, selection of suitable features may significantly affect the performance of the process. Simulations were conducted with 5db and 10db SNRs. Test and ...
متن کاملAutomatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI
Background: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides functional information on the microcirculation in tissues by analyzing the enhancement kinetics which can be used as biomarkers for prostate lesions detection and characterization.Objective: The purpose of this study is to investigate spatiotemporal patterns of tumors by extracting semi-quantitative as well as w...
متن کاملAutomatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers
Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings...
متن کاملAutomatic detection of hypernasal speech signals using nonlinear and entropy measurements
Automatic hypernasality detection in children with Cleft Lip and Palate is classically performed by means of acoustic analysis; however, recent findings indicate that nonlinear dynamics features could be useful for this task. In order to continue deepening in this issue, in this paper the discriminant capability of 4 different nonlinear dynamics features along with a set of 6 entropy measuremen...
متن کامل