Combining multiple kernel models for automatic intelligibility detection of pathological speech

Automatic detection of pathological voice is a challenging task in speech processing. Appropriate acoustic cues of voice can be used to differentiate between normal voices and pathological voices. We propose a method to represent each speech utterance using three types of speech signal representations (i.e., cross-correlation matrix, Gaussian distribution and linear subspace) respectively. Various kernels were applied to these representations for measuring resemblance and difference. Four classifiers, i.e., KNN, kernel partial least squares, kernel SVM, and logistic regression, are studied for comparing their performance of classification. Finally, a simple fusion of learning classifiers from different acoustic representations was carried out at the score decision level for enhancing the performance. The different classifiers were evaluated on the Interspeech 2012 challenge development data set and test data set. Their effects in a fusion scheme are studied. The accuracy of the fusion system attained 78.0 % on test set, with an improved gain of 9.1 % over the challenge baseline 68.9 %.