a novel hybrid method for vocal fold pathology diagnosis based on russian language
Authors
abstract
in this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. then, for optimizing the initial feature vector, a genetic algorithm is proposed. some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and k-nearest neighbours) and the different feature vectors (the initial and the optimized ones). finally, a hybrid of the ensemble of decision tree and the genetic algorithm is proposed for vocal fold pathology diagnosis based on russian language. the experimental results show a better performance (the higher classification accuracy and the lower response time) of the proposed method in comparison with the others. while the usage of pure decision tree leads to the classification accuracy of 85.4% for vocal fold pathology diagnosis based on russian language, the proposed method leads to the 8.5% improvement (the accuracy of 93.9%).
similar resources
A novel hybrid method for vocal fold pathology diagnosis based on russian language
In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neig...
full textA novel hybrid method for vocal fold pathology diagnosis based on russian language
In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies, which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest nei...
full textA HMM-Based Method for Vocal Fold Pathology Diagnosis
Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation’s methods. There are different approaches for vocal fold pathology diagnosis. This paper presents a method based on hidden markov model which classifies speeches into two classes: the normal and the pathological. Tw...
full textA HTK-based Method for Detecting Vocal Fold Pathology
INTRODUCTION In recent years a number of methods based on acoustic analysis were developed for vocal fold pathology detection. These methods can be categorized in two categories:a) detection based on the phonemes b) detection based on the continuous speeches. While there are many researches which belong to the first category, there are few efforts for detecting vocal fold pathology based on the...
full textA Novel Method for Feature Extraction in Vocal Fold Pathology Diagnosis
Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches for vocal fold pathology diagnosis. These algorithms usually have two stages which are Feature Extraction and Classification. While the second stage implies a choice of a var...
full textA Novel GMM-Based Feature Reduction for Vocal Fold Pathology Diagnosis
Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches and algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the th...
full textMy Resources
Save resource for easier access later
Journal title:
journal of ai and data miningPublisher: shahrood university of technology
ISSN 2322-5211
volume 2
issue 2 2014
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023