Evaluation of a method for measuring speech quality based on an authentication approach using a correlation criterion

Abstract

When interacting with the Smart environment using speech interfaces, one of the important aspects is to assess the quality of the pronunciation of phrases. Therefore, obtaining objective quantitative estimates of the proximity to the initial standard as one of the quality measures is relevant. The paper proposes a new method for assessing the quality of phrases pronunciation based on a user authentication approach using deep learning of neural networks. This approach can be used to assess the proximity of the presented sample within the Smart-environment in relation to the initial standard. Such an assessment can be useful both in determining the quality of pronunciation of repeated phrases in relation to the reference one (for example, for assessing the channel used when interacting with the Smart environment or for assessing the quality of the speaker's speech to identify qualitative changes related to his condition or health), so and directly during the procedure for confirming the identity of the speaker. A significant correlation of a new approach for these tasks in comparison with the existing ones based on speech recognition (for quality assessment tasks) is shown