Abstract
Speech-in-noise comprehension difficulties are common among the elderly population, yet traditional objective measures of speech perception are largely insensitive to this deficit, particularly in the absence of clinical hearing loss. In recent years, a growing body of research in young normal-hearing adults has demonstrated that high-level features related to speech semantics and lexical predictability elicit strong centro-parietal negativity in the EEG signal around 400 ms following the word onset. Here we investigate effects of age on cortical tracking of these word-level features within a two-talker speech mixture, and their relationship with self-reported difficulties with speech-in-noise understanding. While undergoing EEG recordings, younger and older adult participants listened to a continuous narrative story in the presence of a distractor story. We then utilized forward encoding models to estimate cortical tracking of four speech features: (1) word onsets, (2) “semantic” dissimilarity of each word relative to the preceding context, (3) lexical surprisal for each word, and (4) overall word audibility. Our results revealed robust tracking of all features for attended speech, with surprisal and word audibility showing significantly stronger contributions to neural activity than dissimilarity. Additionally, older adults exhibited significantly stronger tracking of word-level features than younger adults, especially over frontal electrode sites, potentially reflecting increased listening effort. Finally, neuro-behavioral analyses revealed trends of a negative relationship between subjective speech-in-noise perception difficulties and the model goodness-of-fit for attended speech, as well as a positive relationship between task performance and the goodness-of-fit, indicating behavioral relevance of these measures. Together, our results demonstrate the utility of modeling cortical responses to multi-talker speech using complex, word-level features and the potential for their use to study changes in speech processing due to aging and hearing loss.
Highlights
Speech perception is fundamentally important for human communication
We found no significant difference in these measures between younger and older participants (z = –0.37, p = 0.71, Mann–Whitney U-test), and no correlation between SSQm score and the proportion of correct responses from the behavioral task (r = –0.13, p = 0.43), or between SSQm and high-frequency hearing loss (r = –0.03, p = 0.92)
In addition to robust tracking of wordlevel features, we found that participants’ performance on the comprehension task (Figure 7) and the associated confidence ratings showed a trend towards a positive correlation with the overall model goodness-of-fit for the attended speech
Summary
Speech perception is fundamentally important for human communication. While speech signals are often embedded in complex sound mixtures that can interfere with speech perception via energetic and informational masking, the auditory system is remarkably adept at utilizing attentional mechanisms to suppress distractor information and enhance representations of the target speechAging Effects on Speech Tracking (e.g., Ding and Simon, 2012a; Mesgarani and Chang, 2012; O’Sullivan et al, 2019). While existing tests generally require identification of isolated words or sentences embedded in noise (e.g., speech-shaped noise or a competing talker), real world speech perception often requires real-time comprehension of multi-sentence expressions, embedded in a reverberant environment, in the presence of multiple competing speakers at different spatial positions. In these scenarios, listeners who need to expend additional time and cognitive resources to identify the meaning of the incoming speech may “fall behind” in comprehension of later parts of the utterance. Behavioral measures that more accurately reflect subjective SIN perception difficulties may require utilization of more realistic, narrative stimuli, and focus on quantifying comprehension, as opposed to simple word or sentence identification (e.g., Xia et al, 2017)
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