Method for Mongolian Speaker Feature Transformation based on Linear Discriminant Analysis

In view of the low degree of discrimination of Mongolian speaker features, the problem of learning too much non-speaker information during the modeling process of Mongolian acoustic model, thereby reducing the recognition rate of the Mongolian speaker adaptive speech recognition system. the speaker adaptive model learns too much unnecessary speaker information, which interferes with the speaker adaptive modeling, thereby reducing the recognition rate of the Mongolian speaker adaptive speech recognition system.

In this regard, this paper proposes a Discriminative Feature Transformation algorithm, constructs a Mongolian speaker feature transformation model, and gives a model training algorithm. The discriminative feature transformation method is used to perform discriminative feature transformation on the extracted Mongolian speaker features to ensure that the dispersion between humans is maximized and the dispersion within humans is minimized, and a more discriminatory Mongolian speaker feature is generated.