  With the increasing demand for audiovisual services, telecom service
providers and application developers are compelled to ensure that their
services provide the best possible user experience. Particularly, services such
as videoconferencing are very sensitive to network conditions. Therefore, their
performance should be monitored in real time in order to adjust parameters to
any network perturbation. In this paper, we developed a parametric model for
estimating the perceived audiovisual quality in videoconference services. Our
model is developed with the nonlinear autoregressive exogenous (NARX) recurrent
neural network and estimates the perceived quality in terms of mean opinion
score (MOS). We validate our model using the publicly available INRS bitstream
audiovisual quality dataset. This dataset contains bitstream parameters such as
loss per frame, bit rate and video duration. We compare the proposed model
against state-of-the-art methods based on machine learning and show our model
to outperform these methods in terms of mean square error (MSE=0.150) and
Pearson correlation coefficient (R=0.931)
