Multivariate Classification of Mild and Moderate Hearing Loss Using a Speech-in-Noise Test for Hearing Screening at a Distance

In the area of smartphone-based hearing screening, the number of speech-innoise tests available is growing rapidly. However, the available tests are typically based on a univariate classification approach, for example using the speech recognition threshold (SRT) or the number of correct responses. There is still lack of multivariate approaches to screen for hearing loss (HL). Moreover, all the screening methods developed so far do not assess the degree of HL, despite the potential importance of this information in terms of patient education and clinical follow-up. The aim of this study was to characterize multivariate approaches to identify mild and moderate HL using a recently developed, validated speech-innoise test for hearing screening at a distance, namely the WHISPER (Widespread Hearing Impairment Screening and PrEvention of Risk) test. The WHISPER test is automated, minimally dependent on the listeners' native language, it is based on an optimized, efficient adaptive procedure, and it uses a multivariate approach. The results showed that age and SRT were the features with highest performance in identifying mild and moderate HL, respectively. Multivariate classifiers using all the WHISPER features achieved better performance than univariate classifiers, reaching an accuracy equal to 0.82 and 0.87 for mild and moderate HL, respectively. Overall, this study suggested that mild and moderate HL may be discriminated with high accuracy using a set of features extracted from the WHISPER test, laying the ground for the development of future self-administered speech-in-noise tests able to provide specific recommendations based on the degree of HL.