This paper addresses the problem of achieving robust distributed speech recognition in the presence of burst-like packet loss. To compensate for packet loss a number of techniques are investigated to provide estimates of lost vectors. Experimental results on both a connected digits task and a large vocabulary continuous speech recognition task show that simple methods, such as repetition, are not as effective as interpolation methods which are better able to preserve the dynamics of the feature vector stream. Best performance is given by maximum a-posteriori (MAP) estimation of lost vectors which utilizes statistics of the feature vector stream. At longer burst lengths the performance of these compensation techniques deteriorates as the temporal correlation in the received feature vector stream reduces. To compensate for this interleaving is proposed which aims to disperse bursts of loss into a series of unconnected smaller bursts. Results show substantial gains in accuracy, to almost that of the no loss condition, when interleaving is combined with estimation techniques, although this is at the expense of introducing delay. This leads to the proposal that, for a distributed speech recognition application, it is more beneficial to trade delay for accuracy rather than trading bit-rate for accuracy as in forward error correction schemes.

The aim of this work is to improve distributed speech recognition accuracy in packet loss by considering the effect of loss on the temporal derivatives of the feature vector. Analysis of temporal derivatives reveals they suffer severe distortion when static vectors are lost in times of packet loss. The application of missing feature theory and soft-decoding techniques are considered for compensating against packet loss at the decoding stage of recognition. An extension to these methods is developed which considers the static, velocity and acceleration components separately. A series of confidence measures for the temporal derivatives is devised and applied within the soft-decoding framework. Experimental results on both a connected digit task and a large vocabulary task demonstrate significant increases in recognition accuracy under a range of packet loss conditions.

This paper presents a combined packet loss compensation system for distributed speech recognition (DSR). Compensation is applied at three stages within the DSR process beginning with interleaving on the terminal device to reduce burst lengths in the received feature vector stream. On the receiver side estimation of missing vectors is applied to reconstruct the feature vector stream prior to recognition. Finally, the decoding process of the recogniser is modified to take into account the varying reliability of these estimated feature vectors. Experiments performed on both the Aurora connected digits task and the WSJCAM0 large vocabulary task show substantial gains in recognition accuracy across a range of packet loss conditions.

An analysis into the effect of packet loss shows that a speech recogniser is able to tolerate large percentages of packet loss provided that burst lengths are relatively small. This leads to the analysis of three types of interleaver for distributing long bursts of packet loss into a series of shorter bursts. Cubic interpolation is then used to estimate lost feature vectors. Experimental results are presented for a range of channel conditions and demonstrate that interleaving offers significant increases in recognition accuracy under burst-like packet loss. Of the interleavers tested, decorrelated interleaving gives superior recognition performance and has the lowest delay. For example at a packet loss rate of 50% and average burst length 20 packets (40 vectors or 400ms) performance is increased from 49.6% with no compensation to 86% with interleaving and cubic interpolation.

This work compares the performance of three compensation methods for speech recognition in the presence of packet loss. Two methods, cubic interpolation and a novel maximum a posteriori (MAP) estimation, aim to restore the feature vector stream in the event of packet loss, while the third technique applies compensation in the decoding stage of recognition through missing feature theory. To improve performance in burst-like packet loss, interleaving is introduced to disperse bursts of loss. Experiments on the ETSI Aurora connected digit task show best performance to be given by a combination of missing feature theory and cubic interpolation. This raises performance from 50.3% to 69.8% at a packet loss rate of 50% and average burst length of 20 packets. Including interleaving further increases performance to over 76%.

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. 1.

The aim of this work is to improve the robustness of speech recognition systems operating in burst-like packet loss. First a set of highly artificial packet loss profiles are used to analyse their effect on both recognition performance and on the underlying feature vector stream. This indicates that the simple technique of vector repetition can make the recogniser robust to high percentages of packet loss, providing burst lengths are reasonably short. This leads to the proposal of interleaving the feature vector sequence, prior to packetisation, to disperse bursts of packet loss throughout the feature vector stream.