Non-intrusive Speech Intelligibility Prediction of Speech with Additive Noise and Reverberation Using Multiple Deep Learning-Based Speech Enhancement

In the field of non-intrusive speech intelligibility estimation, we have proposed a model that uses speech enhancement as an auxiliary signal for prediction. In this study, we performed intelligibility estimation using multiple speech enhancers, not just a single one, with the expectation that combi...

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Bibliographic Details
Published in2023 IEEE 12th Global Conference on Consumer Electronics (GCCE) pp. 918 - 920
Main Authors Nakazawa, Kazushi, Kondo, Kazuhiro
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.10.2023
Subjects
Additive noise
Correlation coefficient
deep neural network
Degradation
Estimation
intelligibility
Interpolation
non-intrusive-estimation
Predictive models
Speech enhancement
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Summary:In the field of non-intrusive speech intelligibility estimation, we have proposed a model that uses speech enhancement as an auxiliary signal for prediction. In this study, we performed intelligibility estimation using multiple speech enhancers, not just a single one, with the expectation that combining multiple enhancers would allow for error interpolation in the presence of various degradation factors such as additive noise and reverberation. As a result, using multiple speech enhancers improved the estimation accuracy compared to using a single model, and we were able to achieve a maximum correlation coefficient of 0.674 for estimation.
ISSN:2693-0854
DOI:10.1109/GCCE59613.2023.10315264