HMM-based channel error mitigation and its application to distributed speech recognition
The emergence of distributed speech recognition has generated the need to mitigate the degradations that the transmission channel introduces in the speech features used for recognition. This work proposes a hidden Markov model (HMM) framework from which different mitigation techniques oriented to wireless channels can be derived. First, we study the performance of two techniques based on the use of a minimum mean square error (MMSE) estimation, a raw MMSE and a forward MMSE estimation, over additive white Gaussian noise (AWGN) channels. These techniques are also adapted to bursty channels. Then, we propose two new mitigation methods specially suitable for bursty channels. The first one is based on a forward-backward MMSE estimation and the second one on the well-known Viterbi algorithm. Different experiments are carried out, dealing with several issues such as the application of hard decisions on the received bits or the influence of the estimated channel SNR. The experimental results show that the HMM-based techniques can effectively mitigate channel errors, even in very poor channel conditions. (C) 2003 Elsevier B.V. All rights reserved.
channel error mitigation distributed speech recognition hidden markov models viterbi algorithm wireless communications forward-backward algorithm