In current large vocabulary continuous speech recognition systems, multivariate Gaussian mixture distributions and context-dependent phones, typically triphones, are used to achieve high accuracy acoustic models. It is crucial to address the problem of how to estimate an extremely large number of model parameters from a limited amount of training data. The traditional approach uses phonetic decision tree based context clustering for reducing free parameters. However, this approach has several problems that might cause system performance degradation. All of these problems are due to the fact that the traditional approach does not efficiently use the limited training data and therefore fails to obtain effective acoustic models.