Patent ID: 7062433

Claim:
A method of modifying HMM models trained on clean speech with cepstral mean normalization to provide models that compensate for simultaneous channel/microphone distortion and background noise (additive distortion) comprising the steps of: providing HMM models trained on clean speech with expstral mean normalization; for each speech utterance calculating the mean mel-scaled cepstrum coefficients (MFCC) vector b over a clean database; adding the mean MFCC vector b to the mean vectors m p,j,k of the original HMM models where p is the HMM index, j is the state index, and k the mixing component index, to obtain non-CMN mean vectors {overscore (m)} p,j,k ; for a given speech utterance calculating an estimate of the background noise vector {tilde over (X)}; calculating the model mean vectors {circumflex over (m)} p,j,k adapted to the noise {tilde over (X)} using {circumflex over (m)} p,j,k =IDFT (DFT ({overscore (m)} p,j,k ⊕DFT ({tilde over (X)})) to get the noise compensated mean vectors; and calculating the mean vector {circumflex over (b)} of the noisy data over the noisy speech space, and removing the mean vector {circumflex over (b)} of the noisy data from the model mean vectors adapted to noise to obtain the target HMM model mean vectors and modifying said HMM models to compensate simultaneously for convolutive distortion and background noise using said HMM model mean vectors.