A new approach to discriminative HMM training for pathological voice classification

This paper presents a new approach that improves discriminative training criterion for Hidden Markov Models, and is oriented to pathological voice identification. This technique is aimed at maximizing the Area under the Curve of a receiver operating characteristic curve by adjusting the model parameters using as objective function the Mahalanobis distance and the distance between means of the underlying probability density functions associated with each class. The results show that the proposed technique significantly outperforms the accuracy in a classification system compared with other training criteria. Results are provided using the MEEIVL voice disorders database.