SPEECH MODELS GENERATED USING COMPETITIVE TRAINING, ASYMMETRIC TRAINING, AND DATA BOOSTING
First Claim
Patent Images
1. A method of training a speech model, comprising:
- obtaining model parameters for the speech model;
processing a known speech input using the speech model with the model parameters to generate a process result;
calculating a distance between a true result and the process result, given the model parameters and the known speech input; and
modifying the model parameters to reduce the distance between the true result and the process result, to obtain a modified model.
1 Assignment
0 Petitions
Accused Products
Abstract
Speech models are trained using one or more of three different training systems. They include competitive training which reduces a distance between a recognized result and a true result, data boosting which divides and weights training data, and asymmetric training which trains different model components differently.
-
Citations
8 Claims
-
1. A method of training a speech model, comprising:
-
obtaining model parameters for the speech model; processing a known speech input using the speech model with the model parameters to generate a process result; calculating a distance between a true result and the process result, given the model parameters and the known speech input; and modifying the model parameters to reduce the distance between the true result and the process result, to obtain a modified model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
Specification