1. A method of recognizing speech encoded as electrical signals, comprising: a) processing input speech signals to extract one or more feature vectors; b) comparing the extracted feature vectors with stored speech models and a task grammar to derive a null hypothesis factor indicating the probability that the input speech is correctly recognized; c) comparing the extracted feature vectors with a general speech model and a garbage loop grammar to derive an alternate hypothesis factor indicating the probability that the extracted feature vectors correspond to the characteristics of general speech; d) normalizing the log difference of the null and alternate hypothesis probability factors by the magnitude of the log likelihood of the null hypothesis factor; and e) rejecting the speech where the difference of the normalized probability factors is less than a rejection factor derived from the utterance length.
2. A method of recognizing speech according to claim 1, wherein the rejection factor comprises a threshold modeled as a polynomial in utterance length T, and wherein a separate rejection threshold v.sub.i, is derived for each interval representing the input utterance lengths.