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

Children’s perception of degraded speech
at normal vs. fast speech rate
Isabel SCHILLER1; Dominique MORSOMME1; Malte KOB2; Angélique REMACLE1,3
1

Faculty of Psychology, Speech and Language Therapy, and Education, Psychology and Neuroscience of
Cognition Research Unit, University of Liège, Belgium
2Erich Thienhaus Institute, Detmold University of Music, Germany
3

Fund for Scientific Research F.N.R.S. Brussels, Belgium

ABSTRACTThis study investigated the effect of degraded listening conditions and speech rate on children’s answer
accuracy and response time in a speech perception task. Fifty-three normally-developing children (aged 5-6
years) listened to 72 pseudo-word pairs presented at two different speech rates (normal and fast) and four
different listening conditions (normal voice in silence [control], dysphonic voice in silence, normal voice inspeech-shaped noise (SSN) at 0 dBA SNR, and dysphonic voice in SSN at 0 dBA SNR). The participants had
to decide whether the pseudo-words were the same (e.g. /filam/ - /filam/) or different (e.g. /mafin/ - /nafin/).
For either speech rate, degraded listening conditions were found to significantly decrease answer accuracy.
Regarding response time, speech rate interacted with listening condition: At fast as opposed to normal speechrate, children responded slower to normal voice in SSN but faster to the three other listening conditions. Ourfindings suggest that speech signal degradations may disrupt children’s speech perception even at normal
speed. Speech rate might influence the extent of listening effort associated with adverse listening conditions.
A good quality and adequate transmission of the speech signal may help children to listen effectively.
Keywords: Speech rate, Signal quality, Speech perception

1. INTRODUCTION
Children’s ability to process spoken language matures until late adolescence (1-2). The younger
the child, the more vulnerable it is to degradations of the speech signal (3). This is problematic aslistening conditions are rarely optimal. Noise and poor signal quality represent typical sources of
acoustic interference in classrooms and other environments (4). With regard to children’s spoken
language development and academic performance, a careful investigation of how such speech signal
degradations affect spoken language processing is crucial. It is also important to assess whether aspeaker may adopt certain speaking styles, such as slow or high speech rate, to improve intelligibility
in noisy environments.
In favorable listening conditions, it has already been shown that a speaker ’s speech rate may
influence children’s spoken language processing (5-6). A study by Hayiou-Thomas and Plunkett (6)tested 6-year old children’s performance in a grammaticality judgement task, presented at normal
versus fast speed (i.e. 50 % duration of normal speed). At fast speed, children detected significantly
fewer grammatical violations than at slow speed. In a more recent study, Haake et al. (5) presented 6year old children with a sentence-comprehension task at normal, slow (i.e. duration expanded to
160 %), and fast speed (i.e. duration compressed to 60 %). Again, children made significantly moreerrors at fast speed compared to slow or normal speed. Contrary to these two studies, Montgomery etal. (7) did not observe such impeding effects in a simple word recognition task, performed by 8 -year
old children. It appears that the negative impact of fast speech rate on children ’s spoken language
processing is restricted to tasks demanding sufficiently high processing costs.
Apart from linguistic complexity, processing costs may rise when the speech signal i s acoustically5926

degraded (8). At the stage of signal transmission, background noise, such as babble -, classroom-, orcafeteria noise, is the major source of acoustic speech degradation faced by children (4). Moreover, aspeaker’s voice impairment, also referred to as dysphonia, may lead to speech signal degradations (9).
This latter form of degradation takes place at an earlier point in time, during the production of speech.
Evidence indicates that listening to speech in noise or a dysphonic speaker may nega tively affectchildren’s performance and response latency in listening tasks, such as phoneme discrimination (910), word recognition (11), word recall (12-13), and sentence- or passage comprehension (14-18).
This effect may be explained by the widely accepted theory that cognitive capacity is limited (8).
When listening to acoustically degraded speech, more capacity may be allocated to the processing of
irrelevant signal features, thus leaving fewer resources to process the relevant information (17).
Little is known about the interaction of speech rate and listening condition on how children processspeech. The aim of the present study was to determine children’s perception of normal and acousticallydegraded speech at two different speech rates (normal versus fast). Performance and response times
were measured in a speech perception task. In the light of the theory of processing capacity limitations,
we hypothesized that acoustic degradations would impede children ’s speech perception performanceand that this effect would be stronger for fast speech rate. Moreover, we expected children to require
longer response times to process degraded speech at fast speed than at normal speed.

2. METHODS
2.1 Participants
The study sample consisted of 53 children, aged five to six years old (M = 6;4, SD = 0;3), whocomplied with the following inclusion criteria: 1. first-grade primary school student, 2. French native
speaker, 3. hearing threshold ≤ 25 dBA for octave frequencies between 500 and 4000 Hz, 4. no historyof auditory impairments or speech-language disorders, 5. normal or above normal receptive lexical
skills (subtest LexR of the Épreuve du Langage Orale (ELO) [Oral Language Assessment] (19), and6. normal or above normal selective attention skills (subtest Attention et Fonctions Executives[Attention and Executive Functions] of the Bilan NEuroPSychologique de L’Enfant 2 (NEPSYII)[Developmental NEuroPSYchological Assessment] (20-21). Children were recruited from fiverandomly selected primary schools in the French-speaking part of Belgium.
2.2 Speech Perception Task
A digitalized version of the Épreuve Lilloise de Discrimination Phonologique (ELDP) (22) wasused for this study. The ELDP is a two-alternative forced-choice (2AFC) speech perception task thatassesses children’s ability to discriminate between similar sounding pseudo-words. It contains twolists of 36 French pseudo-word pairs. The first list is presented at normal speech rate; the speed
reference was provided by another listening task designed for 4-8 year old children, the EDP 4-8 (23).
The second list is presented at fast speech rate; those items were recorded ~30 % faster than normal
speed items (22). Pseudo-word pairs consist of either two identical items (e.g. /zil/ - /zil/) or twoslightly different items (e.g. /zil/ – /zij/), with a length of up to three syllables. Children are instructed
to listen to the pseudo-word pairs and decide, whether items were identical or different.
2.3 Listening Conditions
For the purpose of our experiment, we newly recorded the two ELDP pseudo-word lists with ahead-mounted condenser microphone (AKG C 544 L), and digitalized them at 44.1 -kHz sampling
frequency with a 16-bit resolution. A female voice expert read the list at normal speech rate and thelist at fast speech rate in her normal voice and while imitating a dysphonic voice. These voice samples
are freely available online (24). Acoustic and perceptual evaluations of the voice qualities confirmed
the absence of a voice problem for the normal voice, and a moderate to severe impairment for thedysphonic voice (25). The two voice qualities were merged with speech-shaped noise (Stipa signal,[26]) at 0 dB SNR. This resulted in four listening conditions:• C1 = normal voice in silence (control)• C2 = dysphonic voice in silence• C3 = normal voice in speech-shaped noise at 0 dB SNR• C4 = dysphonic voice in speech-shaped noise at 0 dB SNR

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Irrespective of speech rate, the two pseudo-words of a pair were separated by a pause of 0.5 seconds.
In speech-in-noise conditions (i.e. C3 and C4), this pause was filled with SSN as shown in Figure 1.
Figure 1 also provides time-related information for normal speed and fast speed items.

Figure 1: Exemplary illustration of a speech-in-noise item consisting of a pair of pseudo-words. The upper
part of the figure shows oscillograms of the noise signal and the speech signals. The lower part of the figure
reports average pseudo-word duration and item duration as a function of speech rate.
2.4 ProcedureChildren were individually tested in a quiet room at school. The speech perception task waspresented with a touch-screen laptop (Dell Latitude 5480) using Open Sesame software (27). Speechstimuli were played back via headphones (AKG K 271 MK II) at comfortable hearing level. Aftereach pseudo-word pair, two response images appeared on the screen, allowing the child to indicate(via touch response) whether the stimuli were identical or different. Each subsequent item was
manually initiated by the experimenter after ensuring that the child was still attentive. In a withinsubjects design, each participant listened to eighteen pseudo -word pairs per listening condition (i.e.
C1-C4), counterbalanced by means of an algorithm provided by Open Sesame (27). This means that
listening conditions varied randomly across items. The first 36 trials were presented at normal speed,
the second 36 trials were presented at fast speed. Outcome variables were task performance (i.e.
probability of correct vs. incorrect responses) and response times of correct trials (i.e. time elapsed
from stimulus offset to touch response). Response time was measured to indirectly evaluate ch ildren’slistening effort related to processing degraded speech at normal and fast speed. Data were analyzed
by means of generalized linear mixed-effects models (GLMM) with a binomial family and logit linkfunction, using R software version 3.3.2 (28).

3. RESULTS
3.1 Task Performance
A GLMM with the fixed effects noise and voice quality revealed a highly significant effect oflistening condition on task performance (χ 2 (3) = 177.16, p ≤ .001). However, the performancedifference regarding normal versus fast speed (i.e. M = .77, SD = .066 versus M = .74, SD = .072) was
not significant (χ 2 (1) = .312, p = .756), nor was there an interaction between speech rate and listening
condition (χ 2(3) = 5.7613, p = .124). Figure 2 presents the mean probability of correct responses withrespect to listening condition and speech rate. For normal speed, task performance gradually declined

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from C1 (M = .89, SD = .025) to C2 (M = .82, SD = .037), C3 (M = .72, SD = .05), and C4 (M = .61,
SD = .057). The GLMM followed by a Tukey HSD post-hoc test, adjusted for multiple comparisons,confirmed that each listening condition at normal speed significantly differed from the others (pvalues ≤ .01). For fast speed, we found a similar decline in task performance from C1 (M =.88, SD= .027) to C2 (M = .76, SD = .044), C3 (M = .64, SD = .056), and C4 (M = .63, SD = .056). Tukey
HSD testing confirmed that C1, C2, and C3 were significantly different from one another, and C4 wassignificantly different from the C1 and C2 (p-values ≤ .01). There was no significant difference
between C3 and C4 (z = -.103, p = .92).

Figure 2: Estimated probability of correct responses as a function of listening condition and speed.
Listening conditions align on the x-axis: normal voice and no noise (C1), dysphonic voice and no noise(C2), normal voice and SSN (C3), and dysphonic voice and SSN (C4). Error bars represent SD.

3.2 Response Time
Regarding response time, a GLMM revealed a significant interaction between speech rate andlistening condition (χ 2(3) = 216.680, p < .001). Tukey HSD post hoc testing, adjusted for multiple
comparisons, showed response time differences between fast and normal speed were significant forC3 (z = 3.14, p = .036) and C4 (z = 10.43, p < .001), but not C1 (z = -1.52, p = .8) or C2 (z = -.49, p= 1.0). The interaction is observable in Figure 3, which presents mean response times as a function oflistening condition and speech rate. Response times for C3 (i.e. normal voice in noise) were on average
107 ms longer at fast versus normal speed (i.e. M = 2018 ms, SD = 19 versus M = 1911 ms, SD = 28).
This pattern was reversed for C4 (i.e. the combination of noise and dysphonic voice), where response
times were on average 160 ms shorter at fast versus normal speed (M = 1906 ms, SD = 23 versus M =2066 ms, SD = 21). Overall, Figure 3 highlights the increase in response times under conditions ofdegraded listening (i.e. C2-C4) as compared to the control condition (C1).

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Figure 3: Estimated response times (in ms) as a function of listening condition and speed. Listening
conditions align on the x-axis: normal voice and no noise (1), dysphonic voice and no noise (2), normal
voice and SSN (3), and dysphonic voice and SSN (4). Error bars represent SD.

4. DISCUSSION
This study examined how speech rate and acoustic signal degradations may influence children ’s
performance and response times in a speech perception task. Children performed worse and r esponded
slower when noise or speaker’s impaired voice interfered with the speech signal. There was a trend
for a negative impact of fast speech rate on children’s perception of degraded speech. Surprisingly, asignificant interaction between listening condition and speech rate revealed that children took moretime to respond to speech-in-noise when listening to the normal voice compared to the dysphonicvoice. These findings are discussed in more detail below.
The observed decline in children’s answer accuracy under conditions of acoustically degradedspeech confirms our hypothesis that acoustic degradations would impede children’s speech perception
performance and supports results from past research (9-11, 13-16). When listening to speech in noise
at 0 dB SNR, children made significantly more processing errors, especially when the speaker ’s voice
was degraded. It seems likely that noise and dysphonic voice may also compromise children ’s speech
perception in real-life situations. If signal degradations hamper the correct analysis of criticalphonemes, higher-level listening comprehension might also be affected. Future experiments with aunder more realistic conditions are needed to confirm this notion.
In line with ELDP reference data (22), children tended to perform better at normal versus fastspeech rate. However, there was no interaction between speech rate and listening condition withrespect to task performance. This surprised us as we expected fast speech rate to pose an additional
challenge for speech perception in already difficult listening conditions. One possibility is that the
speed difference between normal and fast speech rate (i.e. 30 %) was too small to yield an effect.
Recall also that pseudo-word pairs were presented in isolation with unlimited response time and shortinterruptions between items. With regard to listening effort and processing costs, this paradigm is lesschallenging than paradigms in which longer speech segments are presented or response time
restrictions are applied. Beyond that, we cannot be certain whether the children’s perception of thetwo speech rates is actually in agreement with the intended speech rates (i.e. normal and fast). This
uncertainty is worth investigating in a future perceptual experiment.
Results did not confirm our hypothesis that children would be slower at processing acousticallydegraded speech at fast speed than at normal speed. What we found was that children required moretime to process speech presented in noise and/or dysphonic voice , which supports the findings of past5930

studies (16, 18). Results also revealed an interaction between listening condition and speech rate, but
this interaction was difficult to interpret. At normal speed, response latencies were longest fordysphonic voice in SSN. At fast speed, however, response latencies were longest for normal voice in
SSN – a condition we assumed to be less effortful. Methodological issues might help explain thisfinding. Recall that the task implied a 2AFC design with encouraged guessing. If our assumption wastrue, and dysphonic voice in SSN at fast speed was indeed most difficult to process, more children
might have fallen back on a guessing strategy, resulting in a performance of ~50 % correct. In thiscase, a high proportion of quick but random correct trials would have fed into the response time
analysis of this condition. A closer investigation of this speculation was beyond the sc ope of this paper.
Taken together, our results indicated that first-grade primary school children were negatively
affected by acoustic degradations when processing speech. The effect of speech rate on the processingof degraded speech remains inconclusive. To this respect, future studies could benefit frommethodological adaptations, such as (1) increasing the speed difference between normal and fastspeech rate, (2) collecting subjective data on the perception of experimentally set speech rate s (3)imposing response time restrictions to increase working memory load, or (4) using a task with longer
speech segments, such as sentences or passages.

5. CONCLUSION
Speech-shaped noise and a speaker’s dysphonic voice may interfere with speech perception inchildren. A combination of noise and dysphonic voice may be especially detrimental. The present
study does not allow a clear conclusion regarding the effect of speech rate on children ’s perception of
degraded speech, although there was a tendency for fast speech resu lting in more speech processing
errors than a normal speech rate. Future research is necessary to explore the interaction between
speech rate and listening condition with regard to response times. Our results may be relevant forvarious kinds of listening situations in which children listen and learn, because impeded speech
perception may potentially affect higher level listening comprehension. They highlight the importanceof monitoring and improving quality and transmission of the speech signal directed towar ds young
listeners. Providing voice training to the speaker may enhance the signal quality, while acoustic room
adjustments that reduce background noise may allow better signal transmission.

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