What is it about?
This paper proposes a novel speech quality prediction metric "CAQoE" 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 degradations before predicting the quality of speech. The proposed metric 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.
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Why is it important?
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 demonstrate 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 a limited-size speech corpus having different contexts available from the NOIZEUS speech database.
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This page is a summary of: CAQoE: A Novel No-Reference Context-aware Speech Quality Prediction Metric, ACM Transactions on Multimedia Computing Communications and Applications, February 2023, ACM (Association for Computing Machinery), DOI: 10.1145/3529394.
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