In this paper, we propose a new type of frame-based that the recognizer fohws the maximum a posteriori
hidden Markov models (HMMs), in which a sequence of
observations are generated using state-dependent autoregressive
feature models. Based on this correlation = dwIo)= dolw) dw)
model, it can be proved that expressing the probability of
a sequence of observations as a product of probabilities of
decorrelated individual observations doesn’t require the
where W is a word string hypothesis for a given acoustic
observation 0. p(0lw) is the acoustic model, and
assumption of frame independence. Under the maximum
likelihood (ML) criteria, we also derived re-estimation
formulae for the parameters (mean vectors, covariance i=l
matrix, and diagonal regression matrice) of the new is the N%am language model. When deriving the
Hh4Ms using an Expectation Maximization (EM)
algorithm. From the formulae, it’s interesting to see that
the new HMMs have extended the standard HMMs by
relaxing the frame independence limitation. Initial
experiment conducted on WSJ20K task shows an
encouraging performance improvement with only 117
additional parameters in all.