1. A method for predicting speech recognition performance in a speech recognition system, the system comprising a recognition engine, a database, a model learning module, and a performance prediction module, the method comprising the steps of: a. determining, by the performance prediction module, at least one feature vector for an input into the speech recognition system, wherein the at least one feature vector includes features that comprise at least two features selected from the group comprising: the number of phonemes, the number of syllables, and the number of stressed vowels; b. creating a prediction model by: i. selecting a set of keywords; ii. computing an other feature vector of desired features for each of the keywords; iii. inputting the other feature vector into the model learning module, wherein the model learning module adjusts parameters to minimize a cost function; and iv. saving the results from the model learning module as the prediction model for prediction of a figure of merit of the input; c. passing the at least one feature vector into the prediction model; d. applying, by the performance prediction module, the prediction model to predict a figure of merit for the speech recognition system, wherein the figure of merit is indicative of the accuracy of performance of the speech recognition system, wherein the figure of merit (fom) is predicted using a mathematical expression fom = ∑ i = 1 N ⁢ a i ⁡ ( x i - b i ) 2 N represents an upper limit on a number of features based on the determined feature vector used to learn the prediction, i represents the index of features, x i represents the i-th feature in the determined feature vector, and the equation parameters a and b are learned values; e. reporting, by the performance prediction module, the predicted figure of merit for the speech recognition system performance; and f. adjusting the recognition engine based on the predicted figure of merit.