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Low-frequency neural tracking of speech amplitude envelope reflects the convolution of evoked responses to acoustic edges, not oscillatory entrainment

The amplitude envelope of speech is crucial for accurate comprehension, and several studies have shown that the phase of neural activity in the theta-delta bands (1 - 10 Hz) tracks the phase of the speech amplitude envelope during listening, a process referred to as envelope tracking. However, the mechanisms underlying envelope tracking have been heavily debated. A dominant model posits that envelope tracking reflects continuous entrainment of endogenous low-frequency oscillations to the speech envelope. However, it has proven challenging to distinguish this from the alternative model that envelope tracking reflects a convolution of evoked responses to acoustic landmarks within the envelope. To address this, we recorded magnetoencephalography while participants listened to natural and slowed speech. First, we found that peaks in the rate of envelope change, acoustic edge landmarks, induced a strong evoked response. We then contrasted central diverging predictions of evoked response and oscillatory models regarding the spectral and temporal extent of neural phase locking in the theta-delta range at different speech rates. Our analyses revealed transient theta phase locking at both speech rates and delta phase locking in slow speech. This pattern was predicted by the evoked response model but not by oscillatory entrainment. Furthermore, the encoding of acoustic edge magnitudes was invariant to contextual speech rate, demonstrating speech rate normalization of acoustic edge representations. Taken together, our results show that an evoked response model provides a better account of neural phase locking to the speech envelope than oscillatory entrainment.


Abstract
The amplitude envelope of speech is crucial for accurate comprehension, and several studies have 1 shown that the phase of neural activity in the theta-delta bands (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) tracks the phase of the speech 2 amplitude envelope during listening, a process referred to as envelope tracking. However, the 3 mechanisms underlying envelope tracking have been heavily debated. A dominant model posits that 4 envelope tracking reflects continuous entrainment of endogenous low-frequency oscillations to the speech 5 envelope. However, it has proven challenging to distinguish this from the alternative model that envelope 6 tracking reflects a convolution of evoked responses to acoustic landmarks within the envelope. To address 7 this, we recorded magnetoencephalography while participants listened to natural and slowed speech. First, 8 we found that peaks in the rate of envelope change, acoustic edge landmarks, induced a strong evoked 9 response. We then contrasted central diverging predictions of evoked response and oscillatory models 10 regarding the spectral and temporal extent of neural phase locking in the theta-delta range at different 11 speech rates. Our analyses revealed transient theta phase locking at both speech rates and delta phase 12 locking in slow speech. This pattern was predicted by the evoked response model but not by oscillatory 13 entrainment. Furthermore, the encoding of acoustic edge magnitudes was invariant to contextual speech Introduction 1 Speech comprehension is essential to human communication. A major computational step in neural 2 processing of speech is tracking of its amplitude envelope, the overall intensity of speech across spectral 3 bands. The speech envelope is dominated by fluctuations in the range of ~ 1 -10 Hz, which cue the 4 syllabic structure of speech, and the removal of which from the speech signal severely impairs 5 intelligibility (Drullman et al., 1994a(Drullman et al., , 1994b. A multitude of studies have shown a consistent relationship 6 between the phase of band-limited low-frequency neural activity measured in M/EEG over auditory 7 cortical areas and the phase of the amplitude envelope of speech, a phenomenon widely known as 8 envelope tracking (Ahissar et al., 2001;Luo & Poeppel, 2007). Subsequent studies found that the strength 9 of envelope tracking is correlated with speech intelligibility, suggesting that it could constitute an implementing automatic prediction of the timing of upcoming speech (e.g., the next syllable (Hovsepyan 20 et al., 2020)). According to this theory, in natural speech, phase resetting of entrained oscillations is dri-21 ven by discrete acoustic landmark events in the speech signal. 22 An alternative theory suggests that representations of the speech envelope reflect the convolution of 23 evoked responses to discrete acoustic landmark events in the speech signal, as has been found previously 24 in the visual domain (Breska & Deouell, 2017;Capilla et al., 2011). These acoustic landmarks could 25 either be amplitude peaks, marking vowel midpoints (Ghitza, 2013), or acoustic edges (i.e., peaks in the 26 rate of amplitude envelope change) that mark the perceived onset of vowels (Mermelstein, 1975). Recent 27 intracranial evidence of acoustic edge encoding in speech cortical areas suggested the latter (Oganian & 28 Chang, 2019), also supported by findings from electroencephalography (Brodbeck et al., 2018). However, 29 to date, due to the rapid dynamics of natural speech, it has not been tested directly whether landmark

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Here, we approach this problem by comparing neural responses to speech at a regular speech rate 33 and equally intelligible speech slowed to 1/3 of its original rate, using magnetoencephalography (MEG) 34 recordings ( Figure 1A). Slowing increased the time interval between consecutive landmark events in the 1 speech envelope, as well as the time interval between different envelope landmarks and other (e.g., 2 phonetic) content in the speech signal, allowing us to examine the effects of this shift on neural phase-3 locking patterns. Prior to examining the predictions of evoked response and oscillatory accounts of 4 envelope tracking, we confirmed that our data exhibited phase locking between speech envelope and 5 neural responses at the frequency of the speech rate in each condition, as reported previously (Peelle & 6 Davis, 2012). Furthermore, we identified the phase-resetting envelope landmark, confirming it to be 7 acoustic edges (Oganian & Chang, 2019). 8 Regarding the main model comparisons, an increase in neural phase locking following phase resets 9 by acoustic landmarks is expected under both evoked response and oscillatory models. However, we 10 hypothesized that the spectral and temporal extent of this phase locking, and its dependency on speech 11 rate, might differ. To derive qualitative predictions, we examined the responses predicted by simple 12 simulation models of an evoked response mechanism and two different oscillatory mechanisms, as 13 previously proposed in the literature: one with a fixed entrainment frequency in the delta range and one 14 with a flexible adjustment of the entrained frequency to the stimulus speech rate ( Figure 1B). We then 15 focused our analyses on patterns of phase locking that diverged between predictions of the three models.

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Examining cortical dynamics in response to slow speech also allowed us to address a second 17 question, namely the neural mechanisms of speech rate normalization. Speech rate normalization allows 18 listeners to adjust perceptual processes, in particular envelope tracking, to differences in speech rate. It 19 has previously been proposed that speech rate normalization is implemented through shifts in the 20 frequency of the phase-locked oscillator to match the speech rate ( Tarantino   2 says…"), with syllable boundaries, amplitude envelope, and first temporal derivative of the envelope superimposed on it. The sam 3 utterance is shown at a regular rate (left) and slowed (right) speech rate. Arrows mark candidate temporal landmark that might ind 4 phase locking (Black: local peaks in the envelope, peakEnv; Purple: acoustic edges, defined as local peaks in the first temporal 5 derivative (rate of change) of the envelope, peakRate). B. Frequency of peakRate/peakEnv events. Dashed vertical lines mark the 6 average frequency of peakRate events in slow (blue, 1.9 Hz) and regular speech (green, 5.7 Hz). C. Single-subject (black) and gro 7 average (red) comprehension performance. D. Sensor selection was based on M100 response to utterance onsets. Top: Group-8 averaged evoked response across all 20 sensors included in the analysis. Error bars are ± 1 SEM across subjects. Bottom: Topogra 9 map of a group-averaged M100 response with selected sensors marked in red. E. Group-averaged evoked response aligned to 10 peakRate and peakEnv events. Dotted lines mark clusters with p < 0.05 with a cluster-based permutation test against 0. Error bars 11 ± 1 SEM across subjects. F. Cerebro-acoustic phase coherence (CAC) between MEG responses and speech envelope (upper panel 12 and the difference between slow and regular speech (∆CAC, lower panel). Data were filtered in semi-logarithmically spaced band 13 between 0.2 and 10 Hz for this analysis. Dashed vertical lines mark the average frequency of peakRate events in each condition, a 14 shown in B. * p < 0.001 with 2-way repeated-measures ANOVA. Error bars are ± 1 SEM across subjects. 15 16

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Speech Envelope Tracking for regular and slow speech as seen in MEG 19 We recorded MEG while participants (n = 12) listened to ten minutes of continuous speech, once at the 20 original rate (regular speech condition) and once slowed to 1/3 of the original speech rate (Slow speech 21 condition, Figure 1A). Stimulus materials were selected from the Boston University Radio Speech Corpus, 22 BURSC (Ostendorf, Price, & Shattuck-Hufnagel, 1995); see Tables S1 and S2 for full stimulus transcript   23 and list of comprehension questions.
In a first step, we characterized the temporal dynamics of acoustic landmark events in our speech 1 stimulus ( Figure S1), with a focus on peaks in the rate of envelope change (peakRate, purple in Figure 1A) 2 and peaks in the envelope (peakEnv, black in Figure 1A). At the regular speech rate, the average interval 3 between landmarks (similar for peakRate and peakEnv) was 175ms, corresponding to an average 4 landmark frequency in the theta band at 5.7 Hz (SD = 2.9 Hz, Figure 1B), in line with the typical power 5 spectrum of the amplitude envelope in natural speech (Ding et al., 2017). When slowed to 1/3 of the 6 original speech rate, the average frequency of landmarks was 1.9 Hz (SD = 1 Hz, similar for peakRate 7 and peakEnv), shifting the peak in the envelope power spectrum to the delta band. Slowing did not impair 8 participants' comprehension behavior, as indicated by responses to 3-4 multiple choice comprehension 9 questions after each story (chance: 50 %, regular speech: mean = 83 %, SD = 13 %; slow speech: mean = 10 90%, SD = 9.5 %; t(11) = -1.85, p = 0.09; Figure 1C). To focus our analyses on sensors that capture 11 auditory sensory processing, we selected ten sensors with the largest M100 response to speech onsets 12 after silence periods from each hemisphere ( Figure 1D) for all further analyses. The M100 response 13 showed the typical dipole pattern in each hemisphere (Chait et al., 2004).

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Acoustic edges drive MEG evoked responses 16 We first asked which landmark in the speech envelope drives evoked responses and phase locking to 17 the envelope in regular speech. First, we tested whether averaging the time-domain neural data (band-pass 18 filtered 1-40 Hz) with alignment to peakRate and peakEnv landmark events led to evoked responses and 19 examined their characteristics. While peakEnv closely follows on peakRate in regular speech, the interval 20 between them varied. Thus, aligning to the incorrect landmark should lead to (1) a reduced magnitude of 21 the averaged evoked neural signal due to smearing and (2) shifts in response onset times away from the 22 acoustic event. We found that alignment to either peakRate or peakEnv led to a robust transient evoked 23 response ( Figure 1E). Crucially, the evoked response was of larger magnitude with alignment to peakRate 24 than to peakEnv (peak magnitude: t(11) = 5.9, p < 0.001). Moreover, this response started after peakRate 25 events, but before peakEnv events (response latency relative to the event for peakEnv: -12.5 ms; peakRate: 26 +50 ms, determined as the first significant time point in a cluster-based permutation test against 0).

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Together, these analyses indicated that peakRate events, not peakEnv events, drove the evoked response.

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To confirm that cortical speech envelope tracking was present in our data (Peelle & Davis, 2012), we 1 calculated the cerebro-acoustic phase coherence (CAC) between neural responses and the speech 2 envelope in frequency bands below 10 Hz. CAC is typically increased at the frequency corresponding to 3 the speech rate (Pefkou et al., 2017), which in our data corresponds to the frequency of peakRate and 4 peakEnv in each rate condition (regular: 5.7 Hz, slow: 1.9 Hz). Indeed, speech rate had opposite effects 5 on CAC in these two frequency bands (2-way repeated-measures ANOVA, interaction F(1, 11) = 31.20, 6 p < 0.001, ߟ ଶ = 0.30, Figure 1F). At 5.7 Hz, CAC was higher for regular speech (t(11) = 5.6, p < 0.001,    in the fixed-frequency oscillator model was wider in regular than in slow speech and spanned the entire 28 delta-theta range, its temporal extent and spectral dynamics clearly differed from the patterns predicted by 29 the evoked response model. To further distinguish between these two models, we analyzed the temporal 30 extent of neural phase-locking predicted by each model for peakRate events with a large temporal 31 distance to neighboring peakRate events (> 500 ms, n = 456 events, roughly 40% of peakRate events in 32 slow speech). In the absence of a new phase reset, the oscillatory model predicted that IEPC would 33 remain increased for multiple oscillatory cycles ( Figure 2D). In contrast, in the evoked response model, 34 the increase in IEPC was temporally limited to the duration of the evoked response. Note that the precise 1 temporal extent of the IEPC increase depends on the decay parameters of the oscillator model. However, 2 a hallmark prediction of oscillatory models is that phase locking will continue after phase reset beyond a 3 single oscillatory cycle. Thus, we focused our analyses on the first two cycles after peakRate events, 4 which was sufficient to distinguish between models. Therefore, it was unnecessary to include a decay 5 parameter in our models (see Methods for additional details).

6
Taken together, the qualitative model comparison identified two divergent model predictions for 7 IEPC patterns in slow speech. Next, we performed these identical analyses on our neural data and 8 compared the patterns in the data against the models' predictions.

10
Delta-Theta phase-locking to acoustic edges is best described by the evoked response model  Figure 1E). To test this, we again focused on those 14 peakRate events in the slowed speech that were at least 500 ms apart from preceding and subsequent 15 peakRate events (n = 456 events, 40 % of all events). For these events, IEPC increases in the theta band 16 (

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Notably, this pattern was evident in single subjects (see Figure S3 for single-subject data

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The perceptual ability to adapt to variation in the speech signal resulting from changes in the speech 33 rate, i.e., the number of syllables produced per second, is referred to as speech rate normalization. 34 1 increases at acoustic edges, that is, lower peakRate magnitudes ( Figure 4A-C      To assess cerebro-acoustic phase coherence between the speech envelope and MEG responses, the 6 speech envelope was processed using the same procedure that was applied to the MEG responses: Down-7 sampling to 400 Hz and band-passed between 0.2 and 10 Hz using the second-order Butterworth filter. 8 We then used the Hilbert transform to extract the phase in the delta-theta frequency range using eighteen

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The evoked response, band-passed power, and phase were segmented into symmetric epochs of 31 1,000 ms duration around onsets of utterances, peakEnv events, and peakRate events, respectively.

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Segments containing sensor data above 1.5 pT and visually identified artifacts (including muscle, eye 33 blink, and motion) were flagged as bad events and removed from further processing (0.2 % of segments).

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For the evoked response and narrow-band power analysis, baseline correction (-200 ms to 0 ms 1 relative to event onset for the evoked response, -400 ms to -200 ms for narrow-band power) was applied 2 before averaging across events. No baseline correction was applied to the instantaneous phase.

4
Sensor selection 5 We selected sensors based on the magnitude of the group averaged M100 waveform, 60 -100 ms 6 after the onset of utterance. To focus all further analyses on responses originating in temporal auditory 7 areas, we selected ten sensors with maximal M100 responses from each hemisphere. 13

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A priori, we hypothesized that CAC would differ between conditions in the frequency bands 20 corresponding to the average frequency of peakRate events in each rate condition (regular: 5.7 Hz; slow: 21 1.9 Hz, Figure 1F). We tested this hypothesis using a 2-way repeated-measures ANOVA with factors 22 frequency band (high/low), factor speech rate (slow/regular).

23
To test for further differences in each frequency band, we assessed the effect of speech rate onto 24 CAC using a repeated-measures t-test between speech rate (slow/regular) across frequency bands.

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Significance in this analysis was Bonferroni-corrected for multiple comparisons across bands. We used the group-averaged N100 response estimated from our data for this model (similar to recent 5 approaches in the field, Doelling et al. 2019). While we found no effect of speech slowing on the shape of 6 the neural response to peakRate events in our previous intracranial work, we assume that neural responses 7 recorded with MEG will be additionally shaped by other speech features that occur in temporal proximity 8 to peakRate events (e.g., vowel onsets  evoked responses and oscillatory activity. In addition to calculating the ERP to peakRate events, we thus 10 also tested whether band-passed power would increase after peakRate events. However, we found no 11 significant effects of peakRate on evoked power in theta or delta bands (p > 0.05, cluster-based 12 permutation test, data not shown). Our hypothesis that this was due to higher susceptibility of power 13 measures to noise was confirmed in a simulation of the evoked response model (see below).
14 We hypothesized that this lack of increase in power in theta or delta bands following peakRate   the drunken driver shows that often those who drink and drive are risk takers. Even when they're sober they're more likely to run red lights and shun the seat belt. If they think they can drink and drive and get away with it, they'll try. The state Safe Roads Act is only a few days old, but it's already facing a challenge.
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