Relax frameindependence assumption for standard HMMs by state dependent auto-regressive feature models

Ying Jia and Jinyu Li

Abstract

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.

Details

Publication typeInproceedings
Published inProc. ICASSP
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