CAQoE: A Novel No-Reference Context-aware Speech Quality Prediction Metric
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Date
2023-01
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Abstract
The quality of speech degrades while communicating over Voice over Internet Protocol applications, for exam ple, Google Meet, Microsoft Skype, and Apple FaceTime, due to different types of background noise present
in the surroundings. It reduces human perceived Quality of Experience (QoE). Along this line, this article
proposes a novel speech quality prediction metric that can meet human’s desired QoE level. Our motivation
is driven by the lack of evidence showing speech quality metrics that can distinguish different noise degra dations before predicting the quality of speech. The quality of speech in noisy environments is improved by
speech enhancement algorithms, and for measuring and monitoring the quality of speech, objective speech
quality metrics are used. With the integration of these components, a novel no-reference context-aware QoE
prediction metric (CAQoE) is proposed in this article, which initially identifies the context or noise type or
degradation type of the input noisy speech signal and then predicts context-specific speech quality for that
input speech signal. It will have of great importance in deciding the speech enhancement algorithms if the
types of degradations causing poor speech quality are known along with the quality metric. Results demon strate that the proposed CAQoE metric outperforms in different contexts as compared to the metric where
contexts are not identified before predicting the quality of speech, even in the presence of limited size speech
corpus having different contexts available from the NOIZEUS speech database.