1. A method, comprising: converting a non-back-off language model to a back-off language model using a background language model, wherein the non-back-off language model assigns a probability to any fixed order n-gram without backing off to lower order n-gram probabilities, and wherein the background language model assigns a non-zero probability to n-grams assigned a zero probability by the non-back-off language model; and pruning the converted back-off language model; wherein the converted back-off language model assigns a probability to a given fixed order n-gram assigned a zero probability by the non-back-off language model and not assigned a fixed order probability by the background language model by backing off to a given lower order n-gram corresponding to the given fixed order n-gram; wherein the given fixed order n-gram comprises a given word and an associated history; wherein the given lower order n-gram comprises the given word and a truncated version of the associated history; wherein lower order n-grams originate from at least one lower order non-back-off language model after converting the lower order non-back-off language model to the back-off language model and highest order n-grams originate from an n-gram language model; and wherein the converted back-off language model is usable for decoding speech, and the converting and pruning steps are executed via a processor device configured to implement at least one speech decoder associated with an automatic speech recognition system configured to integrate the converted back-off language model into a decoding process.