Abstract
In this paper, a training method for continuous mixture density HMMs, named optimal discriminative training (ODT), and its implementation for speech recognition in noise are described. ODT is one of corrective learning methods, applied to continuous mixture density HMMs, and these HMMs are especially useful for speaker-independent speech recognition. Under noisy environments, the recognition categories are liable to confuse, so by using ODT the improvement of recognition accuracy is more expected. Here, we describe the training algorithm of ODT, and the effects of ODT to improve the robustness for adverse environments by the word recognition experiments in noise.
