An Analysis of Packet Loss Models for Distributed Speech Recognition

The evaluation of packet loss compensation techniques for distributed speech recognition requires an effective model of packet loss that is capable of reproducing the burst-like occurrence of loss. Several models have been applied to this task and are based on two or three state Markov chains or Markov models. This work reviews these models in terms of their channel characteristics such as the probability of packet loss and average burst length. Validation of the models is made against both GSM error patterns and a wireless LAN channel which demonstrates effective simulation. A series of speech recognition tests show that similar performance is obtained on the real and simulated channels using the packet loss models. Finally a set of model parameters is presented which allows testing across a range of channel conditions.