Non-intrusive Speech Quality Assessment for Super-wideband Speech Communication Networks
Gabriel Mittag, Sebastian Möller · 2019
The quality of speech communication networks has recently improved significantly by extending the available audio bandwidth from narrowband, firstly to wideband, and then to super-wideband. This bandwidth extension marks the end of the typically muffled sound we know from plain old telephone services. Another reason for increased speech quality is the fully digitally packet-based transmission. However, so far, no speech quality prediction model is able to estimate super-wideband quality without a clean reference signal. In this paper, we present a non-intrusive speech quality assessment model NISQA, which - in contrast to current state-of-the-art models - can predict the quality of super-wideband speech transmission. Furthermore, it is able to accurately predict the quality impact of packet loss concealment of modern codecs, such as Opus and EVS. The model uses a novel approach, where a CNN firstly estimates the per-frame quality, and subsequently, an RNN aggregates the per-frame values over time, to estimate the overall speech quality. Averaged over a comprehensive test set, the model achieves an RMSE*3rd of 0.29 with subjective MOS.