HMM-based methods for channel error mitigation in distributed speech recognition
Distributed Speech Recognition involves the development of techniques to mitigate the degradations that the transmission channel introduces in the speech features. This work proposes an HMM framework from which different mitigation techniques oriented to bursty channels can be derived. In particular, two MMSE-based and a new Viterbi-based mitigation procedures are derived under this framework. Several implementation issues such as the channel SNR estimation or the application of hard decision on the received signal vectors are dealt with. Also, different boundary conditions suitable for the speech recognition application are studied for the different mitigation procedures. The experimental results show that the HMM-based techniques can effectively mitigate channel errors, even in very poor channel conditions.