Auditory, Cognitive, and Linguistic Factors Predict Speech Recognition in Adverse Listening Conditions for Children With Hearing Loss

Abstract

Objectives: Children with hearing loss listen and learn in environments with noise and reverberation, but perform more poorly in noise and reverberation than children with normal hearing. Even with amplification, individual differences in speech recognition are observed among children with hearing loss. Few studies have examined the factors that support speech understanding in noise and reverberation for this population. This study applied the theoretical framework of the Ease of Language Understanding (ELU) model to examine the influence of auditory, cognitive, and linguistic factors on speech recognition in noise and reverberation for children with hearing loss.Design: Fifty-six children with hearing loss and 50 age-matched children with normal hearing who were 7-10 years-old participated in this study. Aided sentence recognition was measured using an adaptive procedure to determine the signal-to-noise ratio for 50% correct (SNR50) recognition in steady-state speech-shaped noise. SNR50 was also measured with noise plus a simulation of 600 ms reverberation time. Receptive vocabulary, auditory attention, and visuospatial working memory were measured. Aided speech audibility indexed by the Speech Intelligibility Index was measured through the hearing aids of children with hearing loss.Results: Children with hearing loss had poorer aided speech recognition in noise and reverberation than children with typical hearing. Children with higher receptive vocabulary and working memory skills had better speech recognition in noise and noise plus reverberation than peers with poorer skills in these domains. Children with hearing loss with higher aided audibility had better speech recognition in noise and reverberation than peers with poorer audibility. Better audibility was also associated with stronger language skills.Conclusions: Children with hearing loss are at considerable risk for poor speech understanding in noise and in conditions with noise and reverberation. Consistent with the predictions of the ELU model, children with stronger vocabulary and working memory abilities performed better than peers with poorer skills in these domains. Better McCreery et al. Speech Recognition in Adverse Conditions aided speech audibility was associated with better recognition in noise and noise plus reverberation conditions for children with hearing loss. Speech audibility had direct effects on speech recognition in noise and reverberation and cumulative effects on speech recognition in noise through a positive association with language development over time.

The impact of mild-to-severe hearing loss on the neural dynamics serving verbal working memory processing in children

“…In the context of children with mild-to-severe hearing loss, Stiles and colleagues ( Stiles et al, 2012 ) found a significant relationship between digit span and vocabulary size in both CNH and CHL. More recently, McCreery et al (2019) found that, in addition to language and auditory factors, phonological working memory ability significantly predicted speech recognition in CHL, which echoed the pattern of results found in an earlier study in children with normal hearing from the same authors ( McCreery et al, 2017 ). Importantly, neither of these studies showed that there were significant differences in working memory ability between CNH and CHL.…”

“…Despite the growing literature suggesting that working memory capacity is significantly linked to language function in those with and without hearing loss ( McCreery et al, 2017 , McCreery et al, 2019 , Stiles et al, 2012 ), there has been no investigation of the neural dynamics underlying these relationships to date. To this end, this study used MEG during a well-established verbal working memory task and a battery of neuropsychological tests to determine the impact of hearing loss on neural oscillatory dynamics that underlie working memory encoding and maintenance processing.…”

“…Basically, CHL recruit additional neural resources and perform at the level of CNH, but at some point of task difficulty or exhaustion of resources, behavioral performance declines. The “point of exhaustion” with regards to neural activity is impossible to quantify, but nonetheless this pattern of activity may explain why some previous studies show differences in working memory performance in those with hearing loss relative to CNH ( Pisoni and Cleary, 2003 ) while others do not ( McCreery et al, 2019 , Stiles et al, 2012 ).…”

“…Building on this idea, the relationship between language measures and working memory capacity has been well-documented in normal- and hard-of-hearing adults ( Akeroyd, 2008 , Besser et al, 2013 , Gordon-Salant and Cole, 2016 , Hwang et al, 2017 ) and children ( Dawson et al, 2002 , Harris et al, 2013 , Kirby et al, 2019 , Lowenstein et al, 2019 , Magimairaj and Nagaraj, 2018 , Magimairaj et al, 2018 , Magimairaj et al, 2020 , McCreery et al, 2017 , McCreery et al, 2019 , Nittrouer et al, 2017 ). In the context of children with mild-to-severe hearing loss, Stiles and colleagues ( Stiles et al, 2012 ) found a significant relationship between digit span and vocabulary size in both CNH and CHL.…”

Investigating the Effect of One Year of Learning to Play a Musical Instrument on Speech-in-Noise Perception and Phonological Short-Term Memory in 5-to-7-Year-Old Children

“…Classroom noise is known to cause distraction and annoyance in children, but its primary effect is a reduction in speech intelligibility (for reviews, see Shield and Dockrell, 2003;Klatte et al, 2013), with a consequently negative impact on academic achievement (Shield and Dockrell, 2008). In typically developing children, the ability to cope with speech in noise (SiN) has been linked to individual differences in cognitive and language abilities (Nelson et al, 2005;Strait et al, 2012;MacCutcheon et al, 2019), age (Corbin et al, 2016), gender (Prodi et al, 2019), and supra-threshold auditory processing abilities (Lorenzi et al, 2000), as well as environmental factors, including reverberation and the spatial, spectral and temporal characteristics of the background noise (MacCutcheon et al, 2018(MacCutcheon et al, , 2019McCreery et al, 2019).…”