Close, G., Hollands, S., Hain, T. et al. (1 more author) (2022) Non-intrusive speech intelligibility estimated by metric prediction for hearing impaired individuals for the clarity prediction challenge 1. In: Interspeech 2022 - 23rd Annual Conference of the International Speech Communication Association. Interspeech 2022 - Human and Humanizing Speech Technology, 18-22 Sep 2022, Incheon, Korea. International Speech Communication Association , pp. 3483-3487.
Abstract
This paper proposes neural models to predict Speech Intelligibility (SI),both by prediction of established SI metrics and of human speech recognition (HSR) on the 1st Clarity Prediction Challenge. Both intrusive and non-intrusive predictors for intrusive SI metrics are trained, then fine tuned on the HSR ground truth. Results are reported on a number of SI metrics, and the model choice for the Clarity challenge submission is explained. Additionally, the relationship between the SI scores in the data and commonly used signal processing metrics which approximate SI are analysed, and some issues emerging from this relationship discussed. It is found that intrusive neural predictors of SI metrics when finetuned on the true HSR scores outperform the non neural challenge baseline.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 ISCA. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council 2429310 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Aug 2022 13:34 |
Last Modified: | 01 Nov 2022 18:02 |
Status: | Published |
Publisher: | International Speech Communication Association |
Refereed: | Yes |
Identification Number: | 10.21437/Interspeech.2022-10182 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189718 |