Abstract
Text-independent voice recognition of the user using short sentences is a very difficult task due to the large spread and inconsistency of the content between short sentences, in order to improve user recognition by voice, it is planned to highlight several sets of distinguishing features that contain more information related to the voice. The results show that the i-vector DNN system is superior to the GMM i-vector system for various durations. However, the characteristics of both systems deteriorate significantly as the duration of the sentences decreases. To solve this problem, we propose two new nonlinear mapping methods that train DNN models to map i-vectors extracted from short sentences to their corresponding i-vectors of long sentences.
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