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.

Featured Image

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.


We believe that our proposed CAQoE metric can be deployed by the internet service providers for measuring and monitoring real-time speech quality in the environments where the speech quality is degraded due to the presence of different types of background noises and then, QoE-aware management actions can be installed to react and maintain the end-user QoE levels.

Mr. Rahul Jaiswal

Read the Original

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.
You can read the full text:



The following have contributed to this page