Robustness of speech quality metrics to background noise and network degradations: Comparing ViSQOL, PESQ and POLQA

By Andrew Hines, Jan Skoglund, Anil C. Kokaram, Naomi Harte. 2013

42 citations recorded by the source corpus.

The Virtual Speech Quality Objective Listener (ViSQOL) is a new objective speech quality model. It is a signal based full reference metric that uses a spectro-temporal measure of similarity between a reference and a test speech signal. ViSQOL aims to predict the overall quality of experience for the end listener whether the cause of speech quality degradation is due to ambient noise, or transmission channel degradations. This paper describes the algorithm and tests the model using two speech corpora: NOIZEUS and E4. The NOIZEUS corpus contains speech under a variety of background noise types, speech enhancement methods, and SNR levels. The E4 corpus contains voice over IP degradations including packet loss, jitter and clock drift. The results are compared with the ITU-T objective models for speech quality: PESQ and POLQA. The behaviour of the metrics are also evaluated under simulated time warp conditions. The results show that for both datasets ViSQOL performed comparably with PESQ. POLQA was shown to have lower correlation with subjective scores than the other metrics for the NOIZEUS database.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric (2020, background): “Since ViSQOL has been presented and benchmarked in a large number of experiments that have validated its application to a number of use cases [7], [8], [16]– [19] we consider it relatively well analyzed for the known datasets, which tend to be smaller and relatively homogeneous.”
Measuring and monitoring speech quality for voice over IP with POLQA, viSQOL and p.563 (2015, methodology): “This work builds on prior work where POLQA, PESQ and ViSQOL were evaluated under a variety of narrowband speech scenarios with different datasets [22, 23, 24].”
Jitter Buffer Compensation in Voice over IP Quality Estimation (2019, background): “adding support for Wideband speech [5], [6], audio quality [7] and spatial audio [8]) but did not address the problem of signal mis-alignment caused by jitter buffers.”