# Characterization of the Intelligibility of
## 4 Discussion
The strength of the approach presented here relies on the combination of computational estimates (STOI) and experimental estimates from subjective listening tests (SRT) to characterize the intelligibility of a speech corpus, including stimuli in different languages. The experimental data support and validate the results obtained using the STOI model; however, performing listening tests undeniably demands a significant amount of time and resources. Therefore, considering the demonstrated high correlation between STOI estimates and experimental estimates tests in multiple experimental settings [43; 52], the STOI measure remains a good surrogate for measuring human speech intelligibility whenever subjective tests are not feasible. It might be argued that, as an intrusive measure, STOI has the major shortcoming of requiring a clean reference signal which is not always available, thus it may limit its applicability in real-world scenarios. However, there is increasing use and development of STOI-based non-intrusive measures [53] which can be a viable solution if the clean reference speech cannot be accessed. Some of these measures may be used to expand the characterization of VCV stimuli here presented (reviewed in [53]), for example, the Coherence Speech-Intelligibility Index [54], the Normalized Covariance-Based Speech Transmission Index [55], and the Frequency-Weighted Segmental SNR [56]. The suitability of VCV recordings in English confirms evidence from previous studies where a significant degree of language independence in English VCV perception was shown, and acoustic and auditory factors have been shown to have a dominant influence in determining perceptual responses even across groups with different native languages (e.g., [32]). Moreover, the use of English VCVs within a speech-in-noise test to be used in multilingual settings is also supported by the widespread diffusion of English as a second language and by the widespread exposure of individuals to English (e.g., through the web and social media). Preliminary analysis of speech recognition performance, as measured using an adaptive 3 AFC task, reveals that the SRTs measured in 12 native and 12 non-native English listeners with similar pure-tone average thresholds (difference \(\leq\) 5 dB HL; mean difference = 1.67 dB HL \(\pm\) 1.44) and similar age (difference \(\leq\) 5 years; mean difference = 2 years \(\pm\) 1.6) were similar (i.e., \(-\)9.1 vs. \(-\)9.2 dB SNR) (details are reported in [5]). The preliminary experimental evidence in non-native listeners reported in [5] confirms that the speech recognition performance of non-native listeners may be similar to that of native listeners when VCV recordings in English are used. It is important to note that such a screening procedure, based on speech stimuli viable for use in listeners of an unknown language, can substantially increase access to screening by eliminating possible barriers related to language, opening the delivery of screening tests at a distance able to identify undetected hearing loss with high accuracy [5; 24]. For example, previous studies have considered an application of the adaptive 3 AFC procedure to a population of 350 participants, including native and nonnative unscreened adults showing that the speech-in-noise test can accurately identify hearing loss of mild and moderate degree with accuracy up to 0.87 and 0.90, respectively [5; 23; 24; 25; 26; 57]. The observed accuracy is higher than that observed for most of the available validated, language-specific speech-in-noise screening tests. For example, with the U.S. version of the digits-in-noise test, the observed sensitivity for mild hearing loss was equal to 0.80, and the specificity was equal to 0.83 [58]. With the original digits in the noise test in Dutch, the sensitivity for mild hearing loss was equal to 0.75,and the specificity was 0.91 [13].