2. The method of claim 1 , wherein the external word embedding neural network language model generates input features from the plurality of previous words based on a concatenation of the external embedding matrix to the history word embedding matrix and provides the input features to a hidden layer of the external word embedding neural network language model.
2. The method of claim 1 , wherein the external word embedding neural network language model generates input features from the plurality of previous words based on a concatenation of the external embedding matrix to the history word embedding matrix and provides the input features to a hidden layer of the external word embedding neural network language model. 3. The method of claim 2 , wherein the plurality of previous words are provided to the external word embedding neural network as word history vectors, w i−2 and w i−1 , and wherein the external embedding neural network language model generates the input features, x i , as follows: 
 x i =[w i−2 R H G w i−1 R H G ], where R H G is the concatenation of the external embedding matrix to the history word embedding matrix.