Determining model-specific factors for assigning classes to test data in speech recognition

摘要

The method uses a probability model to evaluate test data and assign them in different classes. Probability values of different models for the same class assignment are evaluated with model-specific factors and combined to form a total probability value. The model-specific factors are determined such that the total probability of assigning the training data to particular classes is a minimum compared to other classes, and erroneous classification of the training data is minimized.