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AN EFFICIENT APPROXIMATION OF THE FORWARD-BACKWARD ALGORITHM TO DEAL WITH PACKET LOSS, WITH APPLICATIONS TO REMOTE SPEECH RECOGNITION
 

Summary: AN EFFICIENT APPROXIMATION OF THE FORWARD-BACKWARD ALGORITHM TO
DEAL WITH PACKET LOSS, WITH APPLICATIONS TO REMOTE SPEECH RECOGNITION
Bengt J. Borgstršom and Abeer Alwan
Department of Electrical Engineering,
University of California, Los Angeles
jonas, alwan@ee.ucla.edu
ABSTRACT
This paper proposes an efficient approximation of the forward-
backward (FB) algorithm, for the purpose of estimating miss-
ing features, based on downsampling statistical models. The
paper discusses the role of Hidden Markov Models (HMMs)
in the estimation process, and presents an approximation to
the FB method by developing HMMs based on lower reso-
lution quantizers, which are obtained through a tree-structure
mapping of quantizer centroids. To illustrate the effective-
ness of the proposed method, we apply it to the problem of
error concealment in remote speech recognition, using the
Aurora-2 database. The FB approximation provides compara-
ble word recognition accuracy results relative to the standard
FB method, while reducing the computational load by a large

  

Source: Alwan, Abeer - Electrical Engineering Department, University of California at Los Angeles

 

Collections: Computer Technologies and Information Sciences