Support Vector Regression (SVR) mapped the consistency measures to objective speech quality scores. Despite these efforts, the correlation obtained from the ...
The output-based speech quality assessment method has been widely used and received increasing attention since it does not need undistorted signals as ...
Output-based Speech Quality Assessment Using Autoencoder and Support Vector Regression ... Quality Assessment Using Autoencoder and Support Vector Regression.
An output-based speech quality assessment metric incorporating autoencoder and support vector regression is implemented using NTT-AT Chinese corpus containing ...
Feb 26, 2024 · Output-based speech quality assessment using autoencoder and support vector regression. Speech Communication, 110:13–20, 2019. Wang et al ...
Results show that the proposed CNN model with attention pooling function outperforms the standardised ITU-T P.563 and the autoencoder-support vector regression ...
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NISQE: Non-Intrusive Speech Quality Evaluator Based on Natural ...
pmc.ncbi.nlm.nih.gov › PMC10301095
Jun 16, 2023 · proposed an output-based SQA method that uses an autoencoder and SVR to map the feature vector to the objective scores [10]. This technique uses ...
In this paper, we propose a new non-intrusive speech quality assessment metric for objective evaluation of speech quality. The originality of proposed scheme ...
Output-based speech quality assessment using autoencoder and support vector regression ... Non-intrusive Speech Quality Assessment with Support Vector Regression.
Oct 23, 2022 · An output-based speech quality assessment metric incorporating autoencoder and support vector regression is implemented using NTT-AT Chinese ...