# Characterization of the Intelligibility of
## 5 Conclusions  
This study proposes an analysis of estimated VCV intelligibility using computational estimates such as the STOI measure in combination with experimental estimates from listening tests to identify specific sets of stimuli that may be applied in speech-in-noise testing in multilingual settings. Results show that the three criteria used for the testing procedure--high intelligibility, low variability of intelligibility, shallow slope and low SRT--are fulfilled by a subset of VCV stimuli in English when a 3 AFC task is used (i.e., average intelligibility from 1% to 8% higher than the other languages; SRT from 4.01 to 2.04 dB SNR lower; average variability up to 4 times lower; slope from 0.35 to 0.68%/dB SNR lower). The criteria used here can help identify stimuli that may compensate for the additional speech recognition effort that non-native listeners may occur into. The results of this study can be used as a basis for developing speech-in-noise tests that can be administered to individuals of varying languages. Moreover, using an adaptive procedure using the VCVs in English in a 3 AFC task, high accuracy for the identification of mild and moderate hearing loss has been documented. Preliminary findings from listening tests in individuals with varying degrees of hearing loss suggest a limited effect of native language on speech recognition performance using the selected subset of VCV recordings. Despite the encouraging results, this study has limitations, particularly in relation to the use of a single computational measure (the STOI) and in relation to the use of single VCV exemplars in each language. Widening the scope of the obtained findings, further research is needed to characterize the intelligibility of these stimuli using different computational measures and a more robust experimental validation in a large sample of non-native listeners with varying degrees of hearing loss to determine the possible effects of hearing loss and native language on VCV recognition.  
Conceptualization, G.R., T.v.W., R.B. and A.P.; methodology, G.R., T.v.W. and A.P.; software, G.R., G.B., R.A. and E.M.P.; validation, G.R., E.M.P. and A.P.; formal analysis, G.R., E.M.P. and A.P.; investigation, E.M.P. and A.P.; resources, T.v.W., A.P. and R.B.; data curation, G.R. and A.P.; writing--original draft preparation, G.R., E.M.P. and A.P.; writing--review and editing, G.R., G.B., R.A., T.v.W., E.M.P., R.B. and A.P.; visualization, G.R.; supervision, T.v.W., R.B. and A.P.; project administration, T.v.W., R.B. and A.P.; funding acquisition, T.v.W. and A.P. All authors have read and agreed to the published version of the manuscript.  
This study was partially supported by Capita Foundation with project WHISPER, \"Widespread Hearing Impairment Screening and PFEvention of Risk\", funded by 2020 and 2022 Auditory Research Grants. Part of this research work was carried out at the ESAT Laboratory of KU Leuven, in the frame of IWT O&O Project nr. 150432 'Advances in Auditory Implants: Signal Processing and Clinical Aspects' and KU Leuven internal funds C2-16-00449 \"Distributed Digital Signal Processing for Ad-hoc Wireless Local Area Audio Networking\". The research leading to these results has received funding from the European Research Council under the European Union's Horizon 2020 research and innovation program/ERC Consolidator Grant: SONORA (No. 773268).