NIDA: A Parametric Vocal Quality Assessment Algorithm over Transient Connections

This paper presents NIDA, a Non-Intrusive Disconnection-aware vocal quality assessment Algorithm. NIDA accurately estimates vocal perceived quality over wireless data networks by discriminating the perceptual effect of a single random packet loss, 2-4 consecutive packet losses (burst) stemming from contentions, and discontinuity entailed by transient loss of connectivity. NIDA properly accounts for transient loss of connectivity experienced by mobile users over wireless data networks, stemming from vertical and horizontal handovers, or when users roam out of the coverage area of the associated infrastructure. To this end, a novel lossy wireless data channel model has been conceived based on a continuous-time Markov model. The channel model is calibrated at run-time based on a set of measurements gathered at packet layer using the header content of received voice packets. The perceived quality under each state is properly quantified, then combined in order to predict quality degradation due to wireless data channel features. Performance evaluation study shows that quality degradation ratings calculated using NIDA strongly correlate with quality degradation ratings calculated based on ITU-T PESQ intrusive algorithm, which mimics tightly subjective human rating behavior.