PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Individual Articulatory Control in Speech Enrichment
Chen SHEN 1; Martin COOKE 2; Esther JANSE 3
1
2

Radboud University, the Netherlands

Ikerbasque (Basque Science Foundation), Spain
3

Radboud University, the Netherlands

ABSTRACT
Individual speakers may use various strategies to enrich their speech while speaking in noise (i.e.,Lombard speech) to improve intelligibility. The resulting acoustic-phonetic changes in Lombard speech arehighly variable amongst different speakers, but it is unclear what causes these talker differences. This study
investigates the potential role of articulatory control in speakers’ Lombard speech enrichment success.
Individuals’ predicted intelligibility in both speaking styles (presented at -5 dB SNR) was measured using a
glimpse-based metric.
Seventy-eight speakers read out sentences in both their habitual style, and in a condition where they wereinstructed to speak clearly while hearing loud speech-shaped noise. A diadochokinetic speech task, that
requires speakers to repetitively produce word or non-word sequences as accurately and as rapidly as possible,
was used to quantify their articulatory control.
Speakers’ predicted intelligibility scores were significantly higher in the Lombard than habitual speech
condition. There was no simple effect of articulatory control on predicted intelligibility, nor an interaction
between speaking condition and articulatory control. However, those with poorer articulatory control
required some practice to maximise their enrichment success. This suggests that articulatory control may
relate to a speaker’s knowledge about the articulatory configuration that will provide the best intelligibility.
Keywords: Speech, Intelligibility, Articulation

1. INTRODUCTION

Speech communication rarely happens in noise-free conditions. Individual speakers often,consciously or unconsciously, use various strategies to enrich their speech in order to improve theintelligibility of their speech when talking in noisy environments (1). Speech produced in noisy
conditions is known as ‘Lombard Speech’ (2), and the acoustic-phonetic changes that occur in
Lombard speech are highly variable amongst different speakers (3). However, what causes these talker
differences, and what impact these differences have on intelligibility, remains unclear. This study
investigates the potential role of articulatory control in speakers’ Lombard speech enrichment success,i.e., the degree to which they can enhance their intelligibility in noise, relative to the intelligibility of
their habitual speaking style.
Articulatory control is often referred to as “the systems and strategies that regulate the productionof speech, including the planning and preparation of movements and the executive of movement plansto result in muscle contraction and structural displacement” (4). Articulatory control in clinicalsettings is often measured through maximum performance speech tasks, such as an oraldiadochokinetic (DDK) task. Despite the debates around whether DDK task is representative enoughfor speakers’ speech motor control abilities (5), DDK remains one of the most widely used tasks inthe assessment of speech motor disorders. The task is relatively easy to conduct and administer (6,7),
and it specifically tests speakers’ speech motor limitations (8).
One way to obtain intelligibility scores for speech (often in the presence of noise) is throughcollecting subjective ratings from normal-hearing or sometimes hearing-impaired listeners. However,1c.shen@let.ru.nl
m.cooke@ikerbasque.org
3 e.janse@let.ru.nl
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the process of acquiring subjective intelligibility ratings is time-consuming and resource-demanding,particularly when assessing speech materials of many speakers. To replace human (subjective)
intelligibility ratings, several objective intelligibility measures have been proposed to predict speech
intelligibility in the presence of background noise. For instance, in earlier studies, the articulationindex (9,10), and the speech-transmission index (11) were the most commonly used metrics. Morerecently, a glimpse-based speech perception model was proposed (12). Through an internal automatic
speech recognition component, the model recognises the speech-dominant spectro-temporal regions,or “glimpses”, in speech that survives energetic (noise) masking, attempting to model human speech
perception in noise (12). Subsequently, several studies have used the output of the initial stage of theglimpsing model, the amount of supra-threshold target speech surviving energetic masking or“glimpse proportion”, as a proxy for intelligibility, aiming to predict speech intelligibility in a more
economical way (e.g., 13,14).
Four glimpse-based metrics were recently evaluated to compare their capability of accounting forthe subjective intelligibility of a variety of speech styles in a range of masker types (15). Of the four,
the high-energy glimpse proportion (HEGP) metric had the highest correlations with human listeners’
judgements across the tested datasets. Given our interest in investigating whether individual’
articulatory control ability predicts speakers’ Lombard speech enrichment success, and due to thelarge number of speakers (N = 78) to evaluate, we opted for the more robust metric (HEGP) for thecurrent study. In addition, we investigated whether speakers’ predicted intelligibility in the two
speaking conditions changed over the course of the list of sentences they were asked to produce.

2. METHODOLOGY
2.1 Data collection
Seventy-eight speakers were recruited and recorded at the Centre for Language Studies lab at Radboud
University. They were all native Dutch speakers, with no speech, hearing, or reading disabilities, nor pastdiagnosis of speech pathology or brain injury, and they all had normal or corrected-to-normal vision.
Participants were reimbursed for their time through course credits or gift vouchers, and all 78 of them gave
informed consent for their audio recordings to be analysed.
A DDK speech task was used to elicit participants’ maximum performance (rate and accuracy) as
an index of their articulatory control. Participants were instructed to repetitively produce two nonwords (‘pataka’, ‘katapa’) and two real (Dutch) words (‘pakketten’ – packages, ‘kapotte’ – broken) as
accurately and as rapidly as possible for around 10 seconds.
A sentence-reading task was used to elicit participants’ habitual and Lombard speech. Participants
were first instructed to read out 48 sentence stimuli correctly and fluently in their habitual style.
Different lists were made to counterbalance the order of the 48 sentences over participants. They werethen asked to read out the same 48 sentences (but in different orders) while being exposed to speechshaped noise (at 78 dB SPL) through a pair of closed headphones (Sennheiser HD 215). In the lattercondition, participants were also told to speak clearly and that their speech would be evaluated at alater stage, and that the top-three most intelligible speakers would get a gift voucher.
Stimuli of the two speech tasks were presented using PowerPoint slides on a 24’’ full HD monitor
placed in front of the participant. Recordings were made using a Sennheiser ME 64 cardioid capsulemicrophone through a pre-amplifier (Audi Ton) onto a steady-state 2 wave/mp3 recorder Roland R05 in a sound-attenuating recording booth. The first author monitored participants’ task progress andcontrolled the changing of stimuli slides outside the recording booth on the stimulus computer.
2.2 Articulatory Control
Participants’ maximum performance (rate and accuracy) in the DDK speech task was used as anindex of their articulatory control. Individual DDK articulation rate and accuracy were analysed
acoustically in Praat (16), based on the first 7-second (or as close to 7-second as possible for therepetition counts to be an integer) of their DDK production. Rate (syllables/sec) was calculated bymultiplying the total number of correct-and-full repetitions of (non)words produced by eachparticipant in the 7-second time window by three (syllables), and divided this number of total syllables
by the actual production time (total-duration minus errors, in-breaths, and pauses longer than 200 ms
between repetitions). Accuracy (fraction) was calculated as number of correct repetitions divided bynumber of all repetitions in the same 7-second time window. Repetitions were only counted as correct
if they were correct repetitions without any pauses longer than 200 ms.

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2.3 HEGP-model predicted intelligibility
HEGP-metric predicted intelligibility scores were obtained per sentence (for the 48 sentences) inboth habitual and Lombard speaking conditions for all speakers at -5 dB SNR. The speech-shaped
noise employed in Lombard speech elicitation was used as the added noise masker for the HEGP
calculation. HEGP scores were calculated based on the contribution of the high-energy glimpses tointelligibility surviving energetic masking. HEGP scores lie between 0 and 1, with higher numbers
indicating higher glimpse proportions escaping energetic masking, thus higher predicted intelligibility.
2.4 Statistical analysis
To investigate whether speakers’ Lombard speech enrichment success can be predicted by theirarticulatory control ability, we fitted two linear mixed-effects models using HEGP model-predicted
intelligibility scores as dependent variable, one taking DDK rate (z-transformed), and the other taking
DDK accuracy (z-transformed) as fixed effect of interest. Speech condition (habitual versus Lombard),and Trial number (the order in which a particular sentence appeared in the reading task) were also
included as fixed factors in these models, as well as their potential interactions with DDK performance.
Additionally, Participant and Sentence were included as random effects in the two models. Model fit
improved for both models by allowing a random by-participant slope for the Condition effect,indicating that speakers indeed differed in their enrichment effect. The two full models (testing for atriple interaction between Condition, Trial and DDK performance) were then stripped in a step-wisemanner (with the insignificant interactions removed first, followed by insignificant effects, startingfrom the ones that had the lowest t-values) to arrive at the most parsimonious model. Modelcomparisons were applied after each removal of the least significant predictor to verify that theexclusion of the predictor (or interaction) did not lead to significantly different model fit.

3. RESULTS

Table 1 presents descriptive data of speakers’ mean performance in the DDK task. Speakers’ mean
intelligibility scores, as predicted by the HEGP metric, are 0.44 (SD = 0.02) for habitual speech and0.54 (SD = 0.05) for Lombard speech respectively.
Table 1 – Summary of task performanceSpeech tasks
DDKRate (syllables/sec)

Accuracy (fraction correct)

MeanSD

MeanSD

6.12

0.82

0.91

0.08

The overall Lombard intelligibility gain can also be seen in Figure 1 below, showing the averaged
HEGP scores for each speaker (each dot represents one speaker’s HEGP scores collapsed over the 48
sentences) in both habitual (x-axis) and Lombard (y-axis) conditions.

Figure 1 – Speakers’ averaged HEGP scores in both speech styles. Note that values above the diagonal line
represent a Lombard intelligibility gain.

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Figure 1 illustrates the variation in intelligibility across speakers, particularly in the Lombard
condition. As mentioned before, the two statistical models on the predicted intelligibility of thespeakers tested whether DDK performance (DDK rate or DDK accuracy) modulated speakers’
enrichment success (as quantified by the difference in predicted intelligibility between the two speech
conditions), and whether speakers’ predicted intelligibility changed over trials. DDK rate did notpredict HEGP intelligibility, nor did it interact with Condition or Trial. However, the model of DDK
accuracy (rather than DDK rate) showed the following results.
Table 2 – Linear mixed-effects model output for predicted speech intelligibility

HEGP Scores
Estimates

Std. Errorp(Intercept: Lombard)

0.54

0.59×10-2<0.001

Habitual-0.10

0.47×10-2<0.001DDK Accuracy-0.23×10-2

0.49×10-2

0.631

Trial

0.17×10-3

0.40×10-4<0.001

ConditionHabitual × DDK Accuracy

0.34×10-2

0.47×10-2

0.471

ConditionHabitual × Trial-0.15×10-3

0.48×10-40.002

DDK Accuracy × Trial-0.87×10-4

0.27×10-40.001

ConditionHabitual × DDK Accuracy × Trial

0.11×10-3

0.39×10-40.006

Predictors

Table 2 confirms that predicted intelligibility in the habitual condition was lower than in theLombard condition, and that predicted intelligibility in the Lombard condition increased over trials
while it remained stable in the habitual condition (as shown in a model in which the habitual speaking
condition was mapped on the intercept). DDK accuracy did not have an overall effect on predicted
intelligibility, but it did interact with the increase in Lombard intelligibility over trials. Morespecifically, the intelligibility increase over trials, as observed in the Lombard condition, was smaller
for those with higher DDK accuracy.

4. DISCUSSION

This study was set up to investigate the potential role of articulatory control in the extent to which
speakers can enrich their speech when changing from their habitual speaking style to speaking clearlyin noise (i.e., their Lombard speech enrichment success). Rather than collecting human (subjective)
intelligibility ratings for our corpus of speakers, we obtained acoustic predictions for the intelligibilityof our 78 speakers’ habitual and Lombard speaking style. As expected, speakers indeed differed intheir speech enrichment success. Furthermore, speakers generally needed some practice to arrive atslightly greater Lombard gains as evident from the trial effect in the Lombard speaking style. It is not
the case that participants with better articulatory control (as indexed by DDK accuracy) were generally
more intelligible, in either speaking condition. However, the ones with better DDK accuracy showed
less improvement of their Lombard intelligibility through practice (over trials). In that sense, DDK
does index speakers’ articulatory control, if articulatory control is seen as speakers’ knowledge ofwhat to do articulatorily to produce more intelligible speech in noise. Further acoustic analyses areneeded to examine whether speakers with poorer articulatory control changed from applying lessappropriate to more appropriate enrichment strategies.

ACKNOWLEDGEMENTS

This project has received funding from the EU’s H2020 research and innovation programme underMSCA GA 675324.

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