When speech signals become noisy, distorted, or challenging to comprehend, the human auditory system compensates by combining high-level cognitive processes and low-level auditory mechanisms. The brain engages a complex network of neural systems to process degraded speech, integrating sensory, cognitive, and linguistic brain regions in a coordinated manner (Obleser and Kotz, 2010; Karunathilake et al., 2023b). Additionally, studies have highlighted that phase-locked responses in the auditory cortex are enhanced during speech comprehension, underscoring the role of neural synchronization in processing complex auditory information (Peelle et al., 2013; Hauswald et al., 2022; Kösem et al., 2023).

In the context of degraded speech, auditory cortical areas, such as the primary auditory cortex (A1) and secondary auditory regions, are primarily responsible for processing the degraded acoustic features. These regions encode basic elements like pitch, duration, and spectral properties, even when noise or reverberation is present. However, when speech intelligibility is compromised—due to background noise or low-quality signals—the brain recruits higher-level cortical regions, particularly in the prefrontal cortex and parietal areas, which involve attentional control, working memory, and top-down modulation. These regions enable the brain to fill perceptual gaps, stabilize auditory representations, and improve the listener’s ability to extract meaningful speech information (Davis and Johnsrude, 2007; Obleser and Kotz, 2010; Karunathilake et al., 2023b). Moreover, robust cortical entrainment to the speech envelope, which relies on the spectro-temporal fine structure, plays a critical role in maintaining speech intelligibility in challenging environments (Ding et al., 2014; Hauswald et al., 2022).

The neural mechanisms extend beyond simple auditory processing by integrating semantic and linguistic expectations to enhance speech intelligibility. For instance, the left inferior frontal gyrus (IFG), associated with syntactic and semantic processing, has been shown to become more active when listeners are attempting to make sense of speech under challenging conditions (Obleser et al., 2007; Peelle et al., 2010; Peelle and Wingfield, 2022). Additionally, the cortical entrainment response is significantly influenced by prior knowledge and speech intelligibility, indicating that listeners’ expectations and contextual understanding are vital for effective neural tracking of speech (Baltzell et al., 2017; Karunathilake et al., 2023b).

While temporal response function (TRF) studies have predominantly used extended speech stimuli, recent research supports the efficacy of shorter sentences (Vanthornhout et al., 2019; Das et al., 2020; Muncke et al., 2022; Slaats et al., 2023; Pendyala et al., 2024). Short sentences have been shown to provide reliable neural tracking responses and are effective in studying speech intelligibility (Muncke et al., 2022; Slaats et al., 2023).

Nevertheless, an important aspect that remains underexplored is how the timing of neural responses contributes to the comprehension of degraded speech. Most previous studies investigating the neural processing of degraded speech have primarily focused on the spatial localization of neural activity, which identifies which brain regions are activated in response to degraded speech. However, neural timing plays a crucial role in speech perception, as it determines how efficiently the brain processes degraded signals over time. Examining the onset, duration, and temporal patterns of neural responses can offer valuable insights into how the brain adapts to distorted auditory input. Understanding these time-varying dynamics is key to revealing how auditory signals are integrated and compensated for, particularly in noisy or unclear conditions.

Important findings into the time course of speech perception have emerged from eye-tracking experiments using the Visual World Paradigm (VWP; Huettig et al., 2011). VWP posits that language is processed incrementally. In spoken words, the initial portions of a speech signal often do not provide enough information to immediately identify the word’s meaning. This initial uncertainty leads to temporary but significant ambiguity among several possible words, resulting in a process known as lexical competition. Multiple potential word candidates are activated during this process and compete for selection. Lexical competition is critical in real-time language processing and is crucial for understanding how listeners decipher speech in everyday environments. Even when a word is clearly pronounced, listeners with normal hearing still experience temporary competition among these possible lexical candidates. The mechanisms through which typical listeners resolve these ambiguities have been extensively studied (e.g., McQueen et al., 1999; Dahan and Magnuson, 2006).

Immediate competition has been observed across various groups, including infants (Swingley et al., 1999; Fernald et al., 2001), adolescents (Sekerina and Brooks, 2007; Rigler et al., 2015), and postlingually deafened adults using cochlear implants (CIs) (Farris-Trimble et al., 2014). However, CI users face unique challenges due to the degraded auditory signal provided by these devices (Wilson and Dorman, 2008). These challenges highlight the differences in how CI users, especially prelingually deaf children, might process spoken language compared to typical listeners. In particular, prelingually deaf children using CIs may experience word recognition processes that differ significantly from typical listeners (McMurray et al., 2017). Despite research on lexical competition across various groups, there is a notable lack of studies investigating the neural mechanisms underlying these processes, especially in cochlear implant users. Addressing this gap is crucial, as understanding the neural basis of these strategies could lead to improvements in CI technology and rehabilitation methods. In recent years, several groups have advanced our understanding of neural responses in cochlear implant users using electrophysiological and neuroimaging approaches (Dimitrijevic et al., 2019; Langner et al., 2021; Prince et al., 2021). These studies provide important context for interpreting neural data in CI users and underscore the relevance of investigating neural processing alongside behavioral outcomes.

Recent auditory neuroscience frameworks suggest that predictive coding may provide a comprehensive explanation for how the brain processes degraded speech (Rao and Ballard, 1999; Friston, 2005). Predictive coding proposes that the brain continuously generates predictions about upcoming sensory input and minimizes the difference between expected and actual input through a process of error correction (Sohoglu and Davis, 2016; Heilbron et al., 2022). In degraded listening conditions, listeners may rely more heavily on top-down predictions to compensate for the reduced acoustic detail. Importantly, predictive coding mechanisms can operate even during passive listening, supporting the idea that linguistic and auditory processing continues automatically without requiring active attention (Todorovic et al., 2011; Auksztulewicz and Friston, 2016; Sedley et al., 2016; Sohoglu and Davis, 2016). This framework aligns with the observed neural tracking patterns in both early and late time windows under degraded speech conditions, offering a strong theoretical basis for understanding speech processing when auditory input is compromised.

Understanding the temporal dynamics of speech perception in CI users is crucial for developing more effective auditory prostheses and improving communication abilities (Sharma et al., 2002b). To address this gap, it is necessary to delve into how the brain responds to various types of speech input, particularly under conditions that replicate the auditory experiences of CI users. Investigating neural responses to different forms of lexical competition in listeners requires analyzing their reactions to continuous speech. In response to speech features, the brain demonstrates neural tracking during language processing (Lalor and Foxe, 2010). This neural tracking can provide insights into how different populations process speech, especially when the auditory signal is less than optimal.

Studies have used speech envelopes to measure neural responses to continuous speech, as these envelopes capture essential temporal dynamics of speech that correlate with EEG signals (e.g., Ding and Simon, 2012a; Di Liberto et al., 2015; O’Sullivan et al., 2015). Recent research showed that older adults exhibit stronger tracking of speech envelopes even in conditions with poorer signal-to-noise ratios (Decruy et al., 2021; Karunathilake et al., 2023a), possibly due to compensatory mechanisms to maintain comprehension (Presacco et al., 2016). These findings suggest that the brain may adapt to degraded auditory conditions by enhancing certain neural processes to maintain speech understanding. This enhanced neural tracking likely reflects the increased cognitive effort required to resolve lexical competition in challenging listening conditions. Additionally, neural tracking has been studied to indicate how the brain processes new linguistic information, particularly in high lexical competition scenarios. Such studies often reveal specific neural signatures, such as the N400 event-related potential, which reflect the cognitive effort involved in integrating challenging or unexpected words within a sentence (Broderick et al., 2018; Koskinen et al., 2020; Weissbart et al., 2020; Toffolo et al., 2022). These late neural tracking responses are typically delayed relative to word onset and are associated with higher cognitive effort required for semantic integration, especially under conditions of high lexical competition (Lau et al., 2008; reviews on N400 responses: Kutas and Federmeier, 2011; Frank et al., 2015; Frank and Willems, 2017).

Researchers often use noise-vocoded speech to replicate the auditory experience of cochlear implant users (Friesen et al., 2001; Rosen et al., 2013). This method allows researchers to simulate the auditory challenges faced by CI users and study how these challenges affect language processing at the neural level. In this study, we employed EEG recordings and speech envelope tracking techniques while presenting participants with both clear and degraded speech sentences. We aim to understand better how degraded auditory input impacts lexical processing by investigating the neural dynamics within the predictive coding framework. We specifically test whether degraded speech primarily elicits increased reliance on top-down predictions and enhanced error correction mechanisms that facilitate speech processing in challenging listening conditions. The findings from this study could contribute to developing more effective and targeted auditory training and rehabilitation strategies for CI users.

Here, we compare the TRF differences between two speech intelligibility conditions. Using scalp EEG, within-subject comparisons between cortical neural tracking of highly intelligible natural sentences and barely intelligible vocoded sentences were performed by extracting temporal envelope-related responses. In the early time range (approximately 100 ms after sound onset), the N100 component (N1), typically associated with early auditory processing and sensory abstraction stages, is expected to show stronger responses in the natural sentences condition (Näätänen, 2001; Krumbholz et al., 2003; Obleser et al., 2006). Similarly, the P2 component (approximately 200 ms after sound onset), which follows the N1 and reflects higher-level auditory processing including attention and stimulus evaluation, is also anticipated to exhibit enhanced responses in the natural speech condition. Although traditionally linked with early sensory processing, stronger N1 and P2 responses in the natural sentence condition could indicate more efficient or immediate engagement with the speech signal, facilitating quicker lexical competition and selection (Commuri et al., 2023).

Moreover, we anticipate stronger late responses in the vocoded sentences condition for the typical trajectory of semantic integration effort (N400, occurring around 300–500 ms after sentence onset). Recent studies employing TRF analyses have revealed not only early components such as N1 and P2, but also prominent later responses (often termed “P400” TRFs) that are thought to parallel the classic N400 ERP associated with semantic processing (e.g., Broderick et al., 2018). We hypothesized that degraded speech would elicit increased reliance on top-down predictions and enhanced error correction mechanisms, reflected in delayed and enhanced late neural responses, as the brain works to resolve increased prediction error (Auksztulewicz and Friston, 2016; Sohoglu and Davis, 2016; Heilbron and Chait, 2018).

We first tested the above hypothesis using sensor-space signals from central electrodes (i.e., FCz, Cz, and CPz) that are commonly employed to investigate auditory cortex activities related to speech perception (Phillips et al., 2000; Näätänen, 2001; Čeponien et al., 2002; Tremblay et al., 2003; Martin et al., 2008; Brandmeyer et al., 2013; Kayser et al., 2015; Jafarpisheh et al., 2016; Khalighinejad et al., 2017; Steinmetzger and Rosen, 2017; Drennan and Lalor, 2019; Etard and Reichenbach, 2019; Mai and Wang, 2019; Synigal et al., 2020). Subsequently, we further tested the same hypothesis in source space within six bilateral cortical regions of interest (ROI) that were selected based on previous studies that demonstrated the involvement of bilateral Heschl’s gyrus (HG), the planum polare (PP), the planum temporale (PT), the supramarginal gyrus (SMG), inferior frontal gyrus (IFG), and the middle temporal gyrus (MTG).

The present study sought to elucidate the time course of degraded speech perception by examining the TRF in response to speech with varying intelligibility. By comparing clear and vocoded speech conditions, we identified distinct neural patterns associated with each condition, offering insights into how the brain manages lexical competition in diverse auditory environments.

Numerous studies have demonstrated that language processing can occur automatically, even when listeners are not actively attending to speech. Neural responses associated with lexical and semantic processing—such as the N400—have been observed in passive listening paradigms, with recent evidence demonstrating that semantic-level markers including N400 and semantic TRFs can be elicited in such contexts (Brodbeck et al., 2018; Yang et al., 2024). Recent work using TRF and continuous speech paradigms further shows that robust neural tracking of linguistic features is present during passive listening, suggesting that core aspects of language comprehension operate automatically and do not require focused attention (Crosse et al., 2016; Brodbeck et al., 2018; Broderick et al., 2018). Importantly, clinical and neuroimaging research confirms that passive auditory paradigms can reliably elicit language lateralization and language-related brain activity (Okahara et al., 2024). In addition, electrophysiological studies have shown that native language advantages in speech processing are evident even in passive listening, supporting the notion that language processing can proceed without active engagement (Yang et al., 2024). Collectively, these findings support the view that passive listening is sufficient to engage the neural mechanisms underlying language processing, allowing researchers to investigate speech comprehension in more naturalistic and ecologically valid settings.

Within a predictive coding framework (Friston, 2005), our findings demonstrate that accurate sensory predictions in clear speech reduce early TRF amplitudes, while increased prediction errors under degraded conditions lead to delayed, amplified late TRF components (P400_TRF). Our findings revealed that early TRF components, specifically N1_TRF and P2_TRF, exhibited stronger responses in the clear speech condition, indicating more effective initial auditory processing when the speech signal is intelligible (Näätänen, 2001; Krumbholz et al., 2003). This aligns with the predictive coding framework, where accurate top-down predictions facilitate rapid and efficient early sensory processing, minimizing prediction error and supporting fluent speech comprehension (Friston, 2005; Auksztulewicz and Friston, 2016; Sohoglu and Davis, 2016; Heilbron et al., 2022). In contrast, the P400_TRF component was delayed and more pronounced in response to degraded speech. Within the predictive coding framework, this late component is interpreted as a neural signature of increased prediction error and the recruitment of higher-order cortical regions to resolve uncertainty (Garrido et al., 2009b; Blank and Davis, 2016; Sohoglu and Davis, 2016; Heilbron et al., 2022). Although this response showed a positive deflection—unlike the canonical N400 observed in traditional ERP studies—such polarity differences are less critical in the context of TRF analyses. TRF analysis estimates the linear stimulus–response mapping (Crosse et al., 2016). As highlighted in previous TRF studies, the timing and functional context of neural responses are more informative than their polarity, which can vary based on analysis methods and referencing schemes (Di Liberto et al., 2015; Crosse et al., 2016; Brodbeck et al., 2018; Broderick et al., 2018). Thus, our key finding is the delayed temporal dynamics of the response, likely reflecting compensatory or additional semantic integration processes as the brain attempts to maintain comprehension when early acoustic features are degraded.

While our paradigm involved passive listening, converging evidence from EEG, MEG, and fMRI studies demonstrates that key markers of linguistic processing—such as the N400 and semantic TRFs—can be elicited even in the absence of explicit tasks (Wacongne et al., 2011; Brodbeck et al., 2018; Heilbron and Chait, 2018; Yang et al., 2024). The late TRF component (P400_TRF) observed in our study is consistent in timing and cortical localization with these established markers of semantic and lexical processing. Although passive listening may not engage all aspects of conscious language comprehension, these findings support the interpretation that our results reflect genuine linguistic processing, not merely low-level auditory prediction.

An interesting dissociation emerged in measurements of TRF model performance between vocoded and natural conditions. MSE of the TRF model was significantly lower for vocoded conditions (vocoded: 1.227 ± 0.095 vs. natural: 1.297 ± 0.121, p = 0.016). Pearson correlation was significantly higher for vocoded speech (vocoded: 0.037 ± 0.011 vs. natural: 0.030 ± 0.009, p < 0.001). This dissociation likely reflects differences in neural processing complexity, consistent with recent findings on speech intelligibility (Karunathilake et al., 2023b). Vocoded speech with reduced acoustic variability and more consistent spectral structure constrains neural processing to envelope-driven mechanisms, resulting in more stimulus-locked and thus more predictable neural responses (lower MSE and higher correlation), while natural speech enables complex linguistic and semantic processing beyond envelope tracking, introducing variability that reduces correlation and increases MSE. Within a predictive coding framework (Sohoglu and Davis, 2016), degraded speech simplifies processing to lower-level sensory predictions relying on prediction error minimization (Blank and Davis, 2016), whereas intact speech engages richer, higher-order mechanisms that support the observed P400_TRF response as a compensatory error-correction process. An important consideration is whether the reduced P400_TRF in the natural condition reflects intelligibility-driven processing or instead arises from repetition suppression and stimulus familiarity effects, given that identical sentence content was presented in both vocoded and natural condition blocks. The component-specific pattern observed in our data argues against a pure familiarity account. Repetition suppression typically manifests as global neural suppression across all components; our findings instead reveal selective enhancement of early components (N1_TRF, P2_TRF) coupled with suppression only of the late P400_TRF component (Grill-Spector et al., 2006; Garrido et al., 2009a). This selective dissociation is inconsistent with global suppression predicted by repetition suppression or familiarity-driven accounts. Furthermore, alpha power did not differ significantly between blocks [t(49) = 0.32, p = 0.75], indicating that systematic changes in neural vigilance or fatigue do not confound the observed condition differences (Engell and Mccarthy, 2014). The source localization of the P400_TRF to the inferior frontal gyrus (IFG)—a region consistently associated with semantic processing, error correction, and prediction error detection during language comprehension rather than novelty detection—further supports an intelligibility-driven interpretation (Thompson-Schill et al., 1997; Hartwigsen et al., 2013; Teghipco et al., 2023). Within the predictive coding framework, the vocoded condition necessitates increased reliance on top-down predictions and error correction, manifested as enhanced P400_TRF activity, while the natural condition provides sufficient acoustic cues for rapid word recognition with minimal prediction error, reducing the need for late error-correction mechanisms (Blank and Davis, 2016; Sohoglu and Davis, 2016). The observed dissociation between component enhancement (N1_TRF, P2_TRF) and suppression (P400_TRF), combined with stable neural state and appropriate source localization, is most parsimoniously explained by intelligibility-driven neural processing rather than repetition suppression or familiarity effects.

The observed patterns at both the sensor and source levels highlight the brain’s dynamic adaptation to varying auditory environments. In clear speech conditions, TRF components likely reflect successful top-down predictions that minimize sensory surprise, supporting rapid word recognition. However, under vocoded conditions, the brain appears to compensate by prolonging processing, recruiting additional cortical resources to support comprehension. This adaptive mechanism is particularly relevant for individuals with hearing impairments, such as CI users, who often experience degraded auditory signals similar to those simulated by vocoded speech in this study (Wilson and Dorman, 2008). The neural adaptability observed here suggests that even under less-than-optimal listening conditions, the brain may reallocate resources to maintain comprehension, albeit with increased cognitive effort (Giraud and Truy, 2002; Alain et al., 2018).

The increased P400_TRF in the vocoded condition may reflect a broader engagement of cognitive resources that supports semantic processing when acoustic cues are degraded. Evidence from continuous speech TRF analyses indicates that late neural components originating from prefrontal and temporal language regions encode semantic and linguistic information, with enhanced responses during high lexical competition scenarios or when acoustic input is compromised (Kegler et al., 2022). This finding is consistent with studies on aging, which show that older adults often exhibit stronger neural tracking of speech envelopes despite poorer signal-to-noise ratios, likely due to compensatory mechanisms aimed at preserving speech comprehension (Presacco et al., 2016; Duta and Plunkett, 2021; Yip et al., 2021). These results suggest that under degraded conditions, the brain increasingly depends on higher-level predictive and semantic processes to support understanding (Ding and Simon, 2013; Peelle and Wingfield, 2016).

It is noteworthy that we used a passive listening condition where subjects watched a silent movie during auditory stimuli were presented. Such conditions are commonly used to divert participants’ attention while still enabling the study of automatic neural responses related to lexical competition and syntactic processing (Pulvermüller et al., 2008; Kong et al., 2014). These previous findings indicate that speech processing can occur without explicit attention, highlighting the robustness of automatic linguistic and lexical processing mechanisms. Our finding is consistent; both short and long-latency TRF components appeared during passive listening, which may suggest speech processing occurs even in the absence of an explicit task. This finding suggests that passive listening paradigms may be a reliable method for studying degraded speech perception, particularly in clinical populations who may have difficulty sustaining attention during active tasks.

Understanding these neural mechanisms is crucial for improving auditory prostheses and rehabilitation strategies for CI users. Enhanced early neural responses in clear conditions could inform the development of auditory training programs to aim at strengthening early prediction-based processing. Conversely, heightened late responses in degraded conditions suggest the importance of interventions that enhance error monitoring and semantic integration. This could include auditory training focused on top-down prediction, or the use of visual or contextual cues to support comprehension under challenging conditions (Sharma et al., 2002a; Giraud and Lee, 2007).

While the present study provides insights into the neural processing of degraded speech, one notable limitation warrants discussion. The behavioral SI test revealed near-ceiling performance in the natural speech condition (mean = 99.65%, SD = 1.01%), precluding examination of correlations between behavioral SI and neural response measures. Future studies employing populations with greater SI variability—such as older adults, individuals with hearing loss, or cochlear implant users—could reveal informative between-subject associations. Additionally, the present study employed normal-hearing young adults, limiting generalizability to other populations with degraded auditory input. Future research should investigate how these neural mechanisms evolve with experience and training, particularly in auditory-impaired populations. Longitudinal studies could provide valuable insights into the neural plasticity associated with degraded speech processing and word recognition strategies. Additionally, exploring the interplay between cognitive factors such as attention, working memory, and neural responses could further elucidate how the brain manages comprehension in challenging listening conditions (Davis and Johnsrude, 2007; McGettigan et al., 2010). Future studies should include explicit word-onset and lexical surprisal predictors to confirm the semantic interpretation of the P400_TRF component (Crosse et al., 2016; Brodbeck et al., 2018; Broderick et al., 2018).

While our findings are well explained by the predictive coding framework, an alternative interpretation is the “wait-and-see” strategy (Norris et al., 2016; McMurray et al., 2017). This approach suggests that listeners may delay lexical decisions and accumulate more acoustic information before committing to a word, especially under degraded conditions (Brodbeck et al., 2018; Broderick et al., 2018). The delayed P400_TRF component observed in our study could reflect this adaptive, conservative processing style. Although our passive listening paradigm does not directly test this hypothesis, the presence of a delayed neural response in degraded conditions is consistent with this interpretation. Future studies employing active tasks or explicit decision-making paradigms could further clarify the contribution of “wait-and-see” mechanisms to speech comprehension under degraded listening conditions.

In conclusion, this study advances our understanding of the neural dynamics involved in predictive processing under varying speech intelligibility conditions. The differential engagement of early and late TRF components across clear and vocoded speech conditions underscores the brain’s flexibility in processing spoken language. These findings have significant implications for developing auditory prostheses and highlight the importance of tailored rehabilitation strategies that account for the cognitive demands of different listening environments.