4. A method for generating speech based on text, implemented at least in part by a computer, the method comprising: building a first language specific decision tree; building a second language specific decision tree; mapping a leaf node from the first tree to a leaf node of the second tree using a Kullback-Leibler divergence (KLD) technique based on a spectral feature located in a subset of less than all of a frequency range for measuring the KLD between two hidden Markov models (HMMs); receiving text in the second language; and generating speech in the second language, for the received text, based at least in part on the mapping the leaf node from the first tree to the leaf node of the second tree.
4. A method for generating speech based on text, implemented at least in part by a computer, the method comprising: building a first language specific decision tree; building a second language specific decision tree; mapping a leaf node from the first tree to a leaf node of the second tree using a Kullback-Leibler divergence (KLD) technique based on a spectral feature located in a subset of less than all of a frequency range for measuring the KLD between two hidden Markov models (HMMs); receiving text in the second language; and generating speech in the second language, for the received text, based at least in part on the mapping the leaf node from the first tree to the leaf node of the second tree. 7. The method of claim 4 wherein the first language comprises Mandarin.