An efficient approximation of the forward-backward algorithm to deal with packet loss, with applications to remote speech recognition

This paper proposes an efficient approximation of the forward-backward (FB) algorithm, for the purpose of estimating missing 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 resolution quantizers, which are obtained through a tree-structure mapping of quantizer centroids. To illustrate the effectiveness 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 comparable word recognition accuracy results relative to the standard FB method, while reducing the computational load by a large factor (> 250 in this case).