Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Class-Incremental Generalized Discriminant Analysis
Neural Computation
Nonparametric Speaker Recognition Method Using Earth Mover's Distance
IEICE - Transactions on Information and Systems
Springer Handbook of Speech Processing
Springer Handbook of Speech Processing
Automated speech analysis applied to laryngeal disease categorization
Computer Methods and Programs in Biomedicine
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Applying SVMs and weight-based factor analysis to unsupervised adaptation for speaker verification
Computer Speech and Language
Wavelet adaptation for automatic voice disorders sorting
Computers in Biology and Medicine
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In this paper identification of laryngeal disorders using cepstral parameters of human voice is researched. Mel-frequency cepstral coefficients (MFCCs), extracted from audio recordings of patient's voice, are further approximated, using various strategies (sampling, averaging, and clustering by Gaussian mixture model). The effectiveness of similarity-based classification techniques in categorizing such pre-processed data into normal voice, nodular, and diffuse vocal fold lesion classes is explored and schemes to combine binary decisions of support vector machines (SVMs) are evaluated. Most practiced RBF kernel was compared to several constructed custom kernels: (i) a sequence kernel, defined over a pair of matrices, rather than over a pair of vectors and calculating the kernelized principal angle (KPA) between subspaces; (ii) a simple supervector kernel using only means of patient's GMM; (iii) two distance kernels, specifically tailored to exploit covariance matrices of GMM and using the approximation of the Kullback-Leibler divergence from the Monte-Carlo sampling (KL-MCS), and the Kullback-Leibler divergence combined with the Earth mover's distance (KL-EMD) as similarity metrics. The sequence kernel and the distance kernels both outperformed the popular RBF kernel, but the difference is statistically significant only in the distance kernels case. When tested on voice recordings, collected from 410 subjects (130 normal voice, 140 diffuse, and 140 nodular vocal fold lesions), the KL-MCS kernel, using GMM with full covariance matrices, and the KL-EMD kernel, using GMM with diagonal covariance matrices, provided the best overall performance. In most cases, SVM reached higher accuracy than least squares SVM, except for common binary classification using distance kernels. The results indicate that features, modeled with GMM, and kernel methods, exploiting this information, is an interesting fusion of generative (probabilistic) and discriminative (hyperplane) models for similarity-based classification.