PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Selective attention in the brainstem and speech-in-noise
comprehension
Marina SAIZ-ALIA1; Antonio Elia FORTE 2; Tobias REICHENBACH1
1Imperial College London, London, U.K

2Harvard University, Cambridge, U.S.A.

ABSTRACT
Understanding speech in noise is a challenging task. Moreover, the ability to understand speech inbackground noise varies considerably from person to person, even for people that have normal audiograms
and hence no measurable hearing loss. Recently we proposed a method for measuring the brainstem’sresponse to natural non-repetitive speech and showed that this response is modulated by selective attention
to one of two competing speakers. Here we investigate to what extent this brainstem response varies fromsubject to subject. We find significant between-subject variation in the amplitude of the brainstem response
to continuous speech, in its latency, signal-to-noise-ratio, as well as in its modulation by selective attention.
This variability may result from impairments in the auditory periphery, such a cochlear synaptopathy, as
well as from damages of the neural pathways in the brainstem and in the central nervous system that areresponsible for sound processing, and may potentially lead to deficits with speech-in-noise comprehension.
Keywords: auditory brainstem response, speech processing, selective attention

12{marina.saiz-alia15, reichenbach}@imperial.ac.uk
aeforte@seas.harvard.edu

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1. INTRODUCTION
People with normal hearing thresholds can nonetheless experience difficulty with understanding
speech in noisy backgrounds. Indeed, 5-10 % of the patients seeking audiological treatmentcomplain of speech-in-noise (SIN) understanding besides having normal audiograms (1) . A potential
source of such supra-threshold hearing deficits is cochlear synaptopathy, the loss of synaptic
connection between the hair cells in the inner ear and the auditory-nerve fibers, which presumably
leads to a degraded neural encoding of the speech signal compromising speech in noise
comprehension (2). Furthermore, central auditory processing disorders and cognitive deficits
affecting attention, memory or language can also be responsible for speech-in-noise deficits.
Auditory-brainstem responses to clicks are used clinically to assess the functioning of theauditory periphery. The effects of stimulus and recording parameters, and patient factors such as age
and gender, on the amplitude and latency of the click-evoked brainstem response are well
documented (4,5). The response can inform on different types of hearing impairments, in particularon sensorineural hearing loss but also on brainstem impairments such as caused by lesions.
To what extent auditory-brainstem responses can inform on other types of speech-in-noise
deficits remains, however, debated. Animal studies have shown that cochlear synaptopathy results ina reduction of wave I of the click-evoked brainstem response (6–8) , as well as in changes in otherbrainstem measures such as a latency shift of wave V with level of background noise and theamplitude of envelope-following responses (11–13). To which extent these findings translate tohumans is, however, unclear: studies on human volunteers have produced conflicting results
regarding the correlation of such brainstem measures with a putative cochlear synaptopathy as wellas with speech-in-noise comprehension (11–17).
We recently proposed a method for measuring the brainstem response at the fundamental
frequency of continuous speech. Furthermore, we demonstrated that this brainstem response ismodulated by selective attention to one of two competing speakers, evidencing a neural mechanismthat contributes to listening in noisy backgrounds. However, we also observed significant variability
in the brainstem response to speech, as well as in its attentional modulation, among our volunteers(3). Because of the involvement of the brainstem response to speech in speech processing and inattention to speech in noise, deficiencies in the response may indicate speech-in-noise deficits. Here
we therefore seek to quantify the variability in different aspects of the speech-evoked auditorybrainstem response.

2. METHODS
2.1

Brainstem response to speech and selective attention

To measure the brainstem response to speech as well as its attentional modulation we followed
our recently developed procedure (3). In brief, we employed empirical mode decomposition (EMD)of the speech stimuli to extract a “fundamental waveform” of the speech signal (3,27) . The
fundamental waveform oscillates, at each time point, at the fundamental frequency of the speech
signal, with a corresponding amplitude. We then recorded the brainstem response to continuous
speech from scalp electrodes placed at the vertex, Cz, as well as the mastoids. The brainstem
response to speech can be quantified through computing the cross-correlation of the fundamental
waveform, as well as its Hilbert transform, with the scalp recordings. The resulting complex crosscorrelation can be characterized in terms of its amplitude and latency. We measured brainstem
responses from 37 volunteers that had no hearing impairment.

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We studied the response to a single-talker male speaker, as well as to two competing speakers,
one male and one female. All stimuli were presented diotically. The stimulation with two competing
voices allowed us to assess the modulation of the brainstem response by selective attention (28) . For
half of the corresponding stimuli, participants attended the male voice, and vice versa for theremaining parts. We computed the attentional modulation as the relative difference in attended toignored conditions for the male voice.

3. RESULTS
3.1

Variability in speech-evoked brainstem activity and in selective attention modulation

We observed considerable variability in the brainstem response to speech as well as in its
modulation by selective attention among the different participants. The brainstem responses of three
exemplary subjects are shown in Figure 1. One subject exhibits a strong brainstem response with asignal-to-noise ratio (SNR) of 18 dB (Figure 1A), the second subject has a medium response (SNRof 14 dB, Figure 1B), and the brainstem response of the third subject is barely measurable (SNR of 4dB, Figure 1C).
Confirming the results from our previous study we found brainstem responses to speech in quiet
at a mean latency of 8.5 ± 0.4 ms, evidencing a subcortical origin of the response (3). Nevertheless,
we observed a significant between-subject variation of this response. In particular, the latency(Figure 2A) and amplitude (Figure 2B) of the brainstem response to speech in quiet varied
considerably among the participants. The coefficient of variation (CV) was 29% and 46%,
respectively.
We further computed the noise floor of the recordings for each participant (gray horizontal areas
Figure 1A-C and Figure 2C). A null model was computed by cross-correlating the brainstem
response to the fundamental waveform of a different story that had not been heard. The noise floor
was determined as the 80th percentile of the complex cross-correlation in the noise model, forlatencies from -100 ms to 100 ms. Both the noise floor and the SNR of the responses, computed
using the peak amplitude as the signal, exhibited a large variance as well (Figure 2C&D; CV: 12%
and 36%, respectively). However, the noise level was not correlated with the peak amplitude of theresponses (p = 0.12).

Figure 1 – Brainstem response to speech in quiet (grey) as well as to two competing speakers, namely forthe attended voice (dark blue) and for the ignored voice (light blue). We show the brainstem responses ofthree representative subjects, one with a high SNR (A), one with a medium SNR (B), and one with a low
SNR (C). The peak amplitude of the brainstem response to speech in quiet is labeled by a red marker, andthe noise floor is marked in grey. The range of the latency of the three responses for each subject isindicated through a vertical grey bar.

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Selective attention can modulate the brainstem response to speech. Confirming the results fromour previous study, we found that selective attention led to a larger response to the attended than to
the ignored speaker (population average). However, we observed large individual differences in theattentional modulation. Indeed, some subjects displayed the opposite modulation, leading tonegative attentional differences, which increased the variability in the sample (CV: 250%). The
differences in attentional modulation did not result from the SNR of the responses as these twovariables were not correlated (p = 0.51).

Figure 2 – Between-subject variability in different aspects of the brainstem response : latency (A),amplitude (B), noise-floor (C), SNR (D) and relative attentional modulation (E). Red markers represent thethree representative subjects that are shown in Figure 1: high SNR (triangle), medium SNR (square), andlow SNR (circle).

4. CONCLUSIONS
We studied the variability in the brainstem response to natural non-repetitive running speech.
Consistent with our previous study we found a mean latency of 8.5 ± 0.4 ms, evidencing a
subcortical origin of the response. We also found a significant attentional modulation of theresponse when participants listened to two competing speakers.
Importantly, we found significant between-subject variation of the amplitude, latency and SNRof the response, as well as of the selective attention modulation. The variability was comparable toindividual differences in subcortical measures previously reported in cochlear synaptopathy studies(13,29) and in speech-in-noise reports (30).
Because the brainstem response to speech is driven by the neural activity in the auditory-nerve
fibers, variation in the response may result from impairments in the auditory periphery such ascochlear synaptopathy. In addition, since the attentional modulation of the brainstem response to
speech relies on neural feedback loops from the cerebral cortex, variability in the attentional
modulation may have more central origins. Future studies are required in order to assess to whatextent the variability in the speech-evoked brainstem activity and its modulation by attention can beexplained by individual measures of peripheral function (such as those proposed for cochlear
synaptopathy), by more central processing, and whether it may account for differences in speech-innoise comprehension.

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ACKNOWLEDGEMENTS
This research was supported by the Royal British Legion Centre for Blast Injury Studies, fromthe “la Caixa” Foundation (LCF/BQ/EU15/10350044) as well as by EPSRC grants EP/M026728/1
and EP/R032602/1.

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