1. A computer-implemented method for generating a multilingual acoustic model for use in a speech recognition system, comprising: providing to a processor from memory a main acoustic model for a first language including a set of probability distribution functions and a probabilistic state sequence model; providing to the processor from memory at least one second acoustic model for a second language including a set of probability distribution functions and a probabilistic state sequence model; in a computer process, replacing each of the probability distribution functions of the at least second acoustic model by one of the probability distribution functions from the main acoustic model and/or each state of the probabilistic state sequence model from the second acoustic model by a state of the probabilistic state sequence model from the main acoustic model based upon a criteria set to form a modified second acoustic model; in a computer process, replacing each of the second probability distribution functions of the at least one second acoustic model by the respective closest one of the first probability distribution functions to obtain a first modified second acoustic model; in a computer process, replacing each of the second states of the second probabilistic state sequence model of the at least one second acoustic model with the respective closest state of the first probabilistic state sequence model of the main acoustic model to obtain a second modified second acoustic model; in a computer process, weighting the first modified second acoustic model by a first weight; in a computer process, weighting the second modified second acoustic model by a second weight; and in a computer process, combining the first modified second acoustic model weighted by the first weight and the second modified second acoustic model weighted by the second weight and the main acoustic model to obtain the multilingual acoustic model.