11. A speech recognition decoder comprising: at least one processor programmed to: generate a finite state machine search space from an N-gram language model with N greater than two, wherein the N-gram language model includes at least one embedded grammar, wherein said finite state machine search space includes statistical language model (SLM) nodes and grammar nodes, each grammar node representing a state associated with one of the at least one embedded grammar; process the SLM nodes using a Hidden Markov Model (HMM) based strategy; and process the grammar nodes using a Recursive Transition Network (RTN) based strategy, wherein the finite state machine search space includes a single grammar node for each of the embedded grammars regardless of a number of contexts in which each of the embedded grammars is represented in the N-gram language model.
11. A speech recognition decoder comprising: at least one processor programmed to: generate a finite state machine search space from an N-gram language model with N greater than two, wherein the N-gram language model includes at least one embedded grammar, wherein said finite state machine search space includes statistical language model (SLM) nodes and grammar nodes, each grammar node representing a state associated with one of the at least one embedded grammar; process the SLM nodes using a Hidden Markov Model (HMM) based strategy; and process the grammar nodes using a Recursive Transition Network (RTN) based strategy, wherein the finite state machine search space includes a single grammar node for each of the embedded grammars regardless of a number of contexts in which each of the embedded grammars is represented in the N-gram language model. 14. The speech recognition decoder of claim 11 , wherein the at least one processor uses a Viterbi based decoding algorithm to process the statistical language model (SLM) nodes.