On a n-gram model approach for packet loss concealment

Abstract

In this paper, 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 training phase, we construct statistical backoff n-gram models. In test phase, the models are used to predict the speech parameters of lost packets. Experiments were performed on 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 backoff 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 up to six consecutive lost packets. In terms of perplexity of the predictive models, 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.