Prediction-based packet loss concealment for voice over IP: a statistical n-gram approach

We investigate the possibility of predicting lost packets for packet loss concealment using n-gram predictive models. Unlike the conventional repetition-based algorithms, the proposed algorithm is based on the Shannon game, which serves as a principle for predicting the speech parameters of lost packets using the previously received parameters. During the training phase, we construct statistical backoff n-gram models. In the test phase, the models are used to predict the speech parameters of lost packets. Experiments were performed on a switchboard telephone speech database and the proposed algorithm is compared with the conventional repetition-based algorithm. The performance is evaluated in terms of the spectral distortion between the original and the predicted (or repeated) speech. The algorithm based on the back-off n-gram models reduces the spectral distortion by 8.7% over the conventional repetition-based algorithm for the first lost packet after receiving one. Further, it maintains about 6.2% improvement for up to six consecutive lost packets. In terms of perplexity of the predictive models, the backoff n-gram approach outperforms the repetition-based algorithm by 8.65%, which is very close to the improvement rate obtained from the spectral distortion measurement.