1. A method of compensating for additive and convolutive distortions applied to a signal indicative of an utterance, comprising: receiving the signal indicative of an utterance; setting an initial value of a channel mean vector to zero; utilizing a portion of frames from the signal to set initial values of a noise mean vector and a diagonal covariance matrix; utilizing the initial values of the channel mean vector, the noise mean vector, and the diagonal covariance matrix to calculate a Gaussian dependent compensation matrix; utilizing the Gaussian dependent compensation matrix to determine new values of the channel mean vector, the noise mean vector, and the diagonal covariance matrix; updating Hidden Markov Model (HMM) parameters based on the new values of the channel mean vector, the noise mean vector, and the diagonal covariance matrix to account for the additive and the convolutive distortions applied to the signal; decoding the utterance using the updated HMM parameters; re-calculating the Gaussian dependent compensation matrix utilizing information obtained during the utterance decoding; adapting the HMM parameters based upon the re-calculated Gaussian dependent compensation matrix; and applying the adapted HMM parameters to decode the utterance and provide a transcription of the utterance.
1. A method of compensating for additive and convolutive distortions applied to a signal indicative of an utterance, comprising: receiving the signal indicative of an utterance; setting an initial value of a channel mean vector to zero; utilizing a portion of frames from the signal to set initial values of a noise mean vector and a diagonal covariance matrix; utilizing the initial values of the channel mean vector, the noise mean vector, and the diagonal covariance matrix to calculate a Gaussian dependent compensation matrix; utilizing the Gaussian dependent compensation matrix to determine new values of the channel mean vector, the noise mean vector, and the diagonal covariance matrix; updating Hidden Markov Model (HMM) parameters based on the new values of the channel mean vector, the noise mean vector, and the diagonal covariance matrix to account for the additive and the convolutive distortions applied to the signal; decoding the utterance using the updated HMM parameters; re-calculating the Gaussian dependent compensation matrix utilizing information obtained during the utterance decoding; adapting the HMM parameters based upon the re-calculated Gaussian dependent compensation matrix; and applying the adapted HMM parameters to decode the utterance and provide a transcription of the utterance. 2. The method of claim 1 , wherein utilizing a portion of frames from the signal to set an initial value of a noise mean vector includes using simple estimates derived from speech-free frames in the utterance.