Here, an analysis of different acoustic features and their influence in automatic identification of hypernasality is shown. Effective feature selection method includes preprocessing of the initial feature space based on statistical independence analysis. Simultaneously, the synthesis of a specialized diagnostic feature is proposed based on analyzing the acoustic emission of the hyper nasal speech. As a result, It is obtained the acoustic features can differentiate with enough precision the pathology. However, the proposed feature does not require training samples and less computational power, as well