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[John Lazzaro]

=========================== TR announcement =================================


REMAP: Recursive Estimation and Maximization of A Posteriori Probabilities
---Applications to Transition-based Connectionist Speech Recognition---

by H. Bourlard, Y. Konig & N. Morgan
   Intl. Computer Science Institute
   1947 Center Street, Suite 600
   Berkeley, CA 94704 
   email: bourlard,konig,morgan at icsi.berkeley.edu

ICSI Technical Report TR-94-064

Abstract

In this report, we describe the theoretical formulation of REMAP, an
approach for the training and estimation of posterior probabilities
using a recursive algorithm that is reminiscent of the EM (Expectation
Maximization) algorithm for the estimation of data likelihoods. Although
very general, the method is developed in the context of a statistical
model for transition-based speech recognition using Artificial Neural
Networks (ANN) to generate probabilities for hidden Markov models (HMMs).
In the new approach, we use local conditional posterior probabilities
of transitions to estimate global posterior probabili- ties of word
sequences given acoustic speech data. Although we still use ANNs to
estimate posterior probabilities, the network is trained with targets
that are themselves estimates of local posterior probabilities. These
targets are iteratively re-estimated by the REMAP equivalent of the
forward and backward recursions of the Baum-Welch algorithm to
guarantee regular increase (up to a local maximum) of the global
posterior probability. Convergence of the whole scheme is proven.

Unlike most previous hybrid HMM/ANN systems that we and others have
developed, the new formulation determines the most probable word
sequence, rather than the utterance corresponding to the most probable
state sequence. Also, in addition to using all possible state
sequences, the proposed training algorithm uses posterior probabilities
at both local and global levels and is discriminant in nature.


The postscript file of the full technical report (66 pages) can be copied
from our (anonymous) ftp site as follows:
ftp ftp.icsi.berkeley.edu
username= anonymous
passw= your email address
cd pub/techreports/1994
binary
get tr-94-064.ps.Z