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 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%)

ОБНАРУЖЕНИЕ ПАТОЛОГИИ РЕЧЕВОГО ТРАКТА НА ОСНОВЕ ГЕНЕТИЧЕСКОГО АЛГОРИТМА И АНСАМБЛЯ ДЕРЕВА РЕШЕНИЙ

A combination of decision tree ensemble and genetic algorithm is proposed for the vocal fold pathology diagnosis by acoustic signals. The experimental results show a better performance (higher classification accuracy) of the proposed method in comparison with the others.Для диагноза патологии речевого тракта по акустическому сигналу предлагается соединение ансамбля дерева принятия решений и генетического алгоритма. Результаты экспериментов подтверждают лучшие показатели (более высокую точность классификации) предложенного подхода по сравнению с другими методами

Диагностика патологии голосового тракта на основе нейронных сетей

There are different algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods, the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel- Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also Principal-Component Analysis (PCA) is used for feature reduction. An Artificial Neural Network is used as a classifier for evaluating the performance of our proposed method.В этой статье представляется метод искусственных нейронных сетей для решения задач диагностики патологии голосового тракта

Диагностика патологии голосового тракта на основе нейронных сетей

There are different algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods, the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel- Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also Principal-Component Analysis (PCA) is used for feature reduction. An Artificial Neural Network is used as a classifier for evaluating the performance of our proposed method