8. A computer-implemented method comprising: training a generic set of hidden Markov models using training data and at least a first vector mapping function, the first vector mapping function removing at least a portion of information not relevant to recognizing speech from feature vectors derived from the training data; training the first vector mapping function based at least in part on the training data while training the set of hidden Markov models; and training a second vector mapping function having a differing degree of freedom from the first vector mapping function based at least in part on the generic set of hidden Markov models.
8. A computer-implemented method comprising: training a generic set of hidden Markov models using training data and at least a first vector mapping function, the first vector mapping function removing at least a portion of information not relevant to recognizing speech from feature vectors derived from the training data; training the first vector mapping function based at least in part on the training data while training the set of hidden Markov models; and training a second vector mapping function having a differing degree of freedom from the first vector mapping function based at least in part on the generic set of hidden Markov models. 10. The method according to claim 8 , further comprising adapting the second vector mapping function during training of the second vector mapping function based on re-estimation of one or more parameters of the second vector mapping function.