Performance of Monosyllabic vs Multisyllabic Diadochokinetic

Exercises in Evaluating Parkinson’s Disease Hypokinetic Dysarthria

from Fluency Distributions

Pedro Gómez-Vilda

, Andrés Gómez-Rodellar

, Daniel Palacios-Alonso

and Athanasios Tsanas

NeuSpeLab, Center for Biomedical Technology, Universidad Politécnica de Madrid,

Campus de Montegancedo, s/n, 28223, Pozuelo de Alarcón, Madrid, Spain

Usher Institute, Medical School, University of Edinburgh, Old Medical School, Teviot Place, Edinburgh, EH8 9AG, U.K.

Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos,

Campus de Móstoles, Tulipán, s/n, 28933 Móstoles, Madrid, Spain

Keywords: Parkinson’s Disease, Speech Diadochokinetics, Hypokinetic Dysarthria.

Abstract: Hypokinetic Dysarthria (HD) is a major debilitating symptom in the vast majority of people diagnosed with

Parkinson's Disease (PD). It has been traditionally evaluated using diadochokinetic exercises to estimate its

degree of severity, among them, the fast repetition of monosyllables as [pa], [ta], and [ka] and multisyllable

sequences as [pataka], [pakata], [badaga] and others alike. However, the real efficiency of these exercises in

differentiating the participant behaviour as pathological or normative has not been investigated in depth. The

aim of the present work is to explore the timely responsive performance of two of these exercises (a

monosyllabic [ta] vs a multisyllabic [pataka]). A method to characterize statistically syllabic and inter-syllabic

interval durations in the execution of these diadochokinetic exercises, based on Kolmogorov-Smirnov

approximations and Jensen-Shannon Divergence has been used to assess the efficiency of both types of

exercises. The results from the evaluation of 24 gender-balanced participants (12 PD and 12 controls) show

that the monosyllabic exercise does not seem to differentiate well, whereas the multisyllabic exercise has a

better differentiation performance. These findings, although relatively preliminary due to the limited sample

size, underline the need to carefully consider the battery of tests towards assessing HD.

1 INTRODUCTION

Parkinson’s Disease (PD) is a neurodegenerative

disorder second in prevalence to Alzheimer’s Disease

(De Lau and Breteler, 2006). Its origin is mainly

caused by the lack of a specific neurotransmitter

known as dopamine in midbrain (Dauer and

Przedboski, 2003), resulting in relevant neuromotor

deterioration affecting body movement (Duffy,

2013). Since the early work of Dr. James Parkinson

(Parkinson, 1817) describing observable neuromotor

alterations in patients of shaking palsy, including

speech problems, most commonly known as

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Hypokinetic Dysarthria (HD). It is a well-established

fact that PD causes considerable alterations in speech

and phonation (Ricciardi et al., 2016, Brabenec et al.,

2017). Roughly, speech alterations may be classified

as dysphonia (alterations to the production of voice),

dysarthria (alterations in the articulation of speech),

dysprosody (alterations in the definition of the

fundamental frequency) and dysfluency (alterations

in the rhythm and in speech blocking). Although these

terms refer to specific and different aspects of

anomalous speech production, as all these effects are

included in HD, this term will be used for the

remainder of this study. The extraction of acoustic

markers caused by HD in PD speech allows to

114

Gómez-Vilda, P., Gómez-Rodellar, A., Palacios-Alonso, D. and Tsanas, A.

Performance of Monosyllabic vs Multisyllabic Diadochokinetic Exercises in Evaluating Parkinson’s Disease Hypokinetic Dysarthria from Fluency Distributions.

DOI: 10.5220/0010380301140123

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS, pages 114-123

ISBN: 978-989-758-490-9

conclude that speech analysis might become a non-

invasive and cost-effective tool to characterize and

monitor PD. There is “compelling evidence to suggest

that speech can help quantify not only motor

symptoms ... but generalized diverse symptoms in

PD” (Tsanas, 2012). There has been a substantial

body of work aimed to characterize PD induced HD,

focusing on diadochokinetic exercises to assess its

degree of severity. Indicative diadochokinetic

exercises include the repetition of monosyllables as

[pa], [ta], and [ka] and multisyllable sequences as

[pataka], [pakata], [badaga] and others of the same

nature and function. Exercises consist of repetitions

of the sequences as fast as possible, and this setup has

been commonly used in PD speech assessment

(Ziegler, 2002). The efficiency of these exercises as a

way of differentiating participant behaviour as

pathological or normative has not been fully

evaluated (for a comprehensive review see Karlsson,

et al., 2020). The aim of the present study is to explore

if the timely responsive evaluation of these exercises

may serve as a reliable biomarker or if different or

better organized protocols would have a better

performance. The main objective of the present study

is to compare the performance of two classical

diadochokinetic exercises as the repetition of a single

syllable […ta…] where an apical-alveolar pattern is

involved, versus the repetition of a multisyllabic

sequence as […pataka…] that presents bilabial and

dorsal-velar patterns. These two exercises may allow

to properly differentiate between PD participants and

Heal Control (HC) participants. The paper is

organized as follows: Section 2 is devoted to describe

the number of participants in the experimental

framework, the speech recording conditions used, the

biometrical characteristics of the participants, and the

statistical methods used in the study. Section 3

describes the results produced by the statistical

analysis of speech recordings. Section 4 focus on

analysing and discussing the results. Section 5

summarises the main conclusions derived from the

present work.

2 MATERIALS AND METHODS

2.1 Materials

Twelve gender-balanced PD participants were

selected from the patient associations of Alcorcón and

Leganés (APARKAM). The inclusion conditions for

HC participants were non-smoking for the last five

years, and not presenting any known laryngeal or

neurological diagnosis. The study was approved by

the Ethical Committee of Universidad Politécnica de

Madrid. Each participant signed a voluntary

participation informed consent. The study was fully

aligned with the Declaration of Helsinki. The

participants were asked to utter two different

exercises, the first one consisting in the repetition of

the syllable [ta] at the fastest speed possible and as

long as they could sustain it, as […tatata…]

(monosyllabic repetition). The second exercise

consisted of repeating the sequence [pataka] as fast

and as long as possible. These two sequences were

selected for being regular and monosyllabic (the

former one) and for involving three different

articulation points (bilabial, apical-alveolar and

dorsal-velar, the latter). These exercises are

especially well suited for the examination of the

speaker’s fluency, as they do not have any meaning

per se. The first one is regular and serves as a

reference both for HC and PD participants. The

second one invokes the three main articulation points

in Spanish (bilabial, dento-alveolar, velar), and it

forces the speaker to change facial, lingual, velar and

jaw positions, to extract meaningful features from the

distribution of time intervals (inter-syllabic and intra-

syllabic). The recordings were taken in the speech

therapist service room at two different locations of the

patient association, no soundproofing or any other

quality-preserving measures were undertaken, except

keeping a silent environment inside the room with

access limited to participants and assistants. The

speech recordings were originally sampled at 50 kHz

with 16 bits of resolution by a phantom-fed wireless

Audio Technica cardioid microphone, and digitized

on a Motu Traveller board. The data were

downsampled to 8 kHz (antialias filtering at 4 kHz

was previously used) to comply with standard

telephone channel conditions, making it compatible

with remote recordings obtained from a smart phone

using the protocol defined in MonParLoc (Palacios

et al., 2020). The participants were divided in four

data sets for the study: 6 male and 6 female HC

participants, and 6 male and 6 female PD participants

as shown in Table 1.

Performance of Monosyllabic vs Multisyllabic Diadochokinetic Exercises in Evaluating Parkinson’s Disease Hypokinetic Dysarthria from

Fluency Distributions

115

Table 1: Participants’ biometrical data. MC: male control

participants; MP: male PD participants; FC: female control

participants; FP: female PD participants; H&Y: Hoehn and

Yahr PD rating scale; State: medication state (on: under

medication; -: not applicable).

Dataset Code Gende

e H&Y State

MC1 M 69 - -

MC2 M 70 - -

MC3 M 68 - -

MC4 M 67 - -

MC5 M 61 - -

MC6 M 68 - -

FC1 F 66 - -

FC2 F 62 - -

FC3 F 65 - -

FC4 F 67 - -

FC5 F 65 - -

FC6 F 65 - -

MP1 M 71 2 on

MP2 M 69 2 on

MP3 M 73 2 on

MP4 M 73 2 on

MP5 M 73 2 on

MP6 M 69 2 on

FP1 F 73 2 on

FP2 F 73 2 on

FP3 F 66 2 on

FP4 F 71 2 on

FP5 F 78 2 on

FP6 F 70 2 on

2.2 Methods

An experimental framework has been devised to test

the relative effects of HD by means of the extraction

of syllabic and inter-syllabic interval durations

estimated from the speech signal produced by the

participants. The main features considered are mean,

standard deviation, skewness, and kurtosis of the

duration of the syllabic and inter-syllabic (silence)

interval distributions, and their normality. The

methodology used in the study is based on the

estimation of the following acoustic characteristics of

the speech recordings:

 The energy profile estimated using the Teager-

Kaiser Energy Operator (TKEO, Dimitriadis,

Potamianos and Maragos, 2009).

 The glottal residual using the Iterative Adaptive

Inverse Filtering (IAIF, Alku et al., 2019).

 The Voiced-Unvoiced Intervals (VUI) using the

zero-crossings function of the Linear Prediction

(LP) residual.

All these characteristics can be considered correlates

showing relevant semantic clues present in speech,

that affect the quality of phonation, the prosody and

the fluency. The TKO, and the VUI, are defined as

𝐸





𝑛



𝑠





𝑛



𝑠



𝑛1



𝑠𝑛 1

𝐹





𝑛



dim𝑧



𝑛



;

(1)

where

𝑟



𝑛





1;𝑠



𝑛



0;

0;otherwise

𝑞



𝑛



𝑟



𝑛



𝑟𝑛1

𝑧



𝑛





𝑞



𝑛



;𝑞



𝑛



0

0;otherwise

(2)

The TKO and the VUI may be used to determine the

inferior and superior syllabic interval limits as

𝐺





𝑛



𝐸





𝑛



𝐹





𝑛



𝑛





𝑛

𝐺





𝑛1



𝜗

𝑎𝑛𝑑 𝐺





𝑛



𝜗

𝑛





𝑛

𝐺





𝑛1



𝜗

and 𝐺





𝑛



𝜗

(3)

to divide the speech signal produced by

diadochokinetic exercise into syllabic (Sy) and

silence (Si) intervals, containing the interval duration

of syllabic segments d

(i) and silence segments d

(i)

𝑑





𝑖



n





𝑛





𝑑





𝑖



𝑛





𝑛





∀𝑑





𝑖



20𝑚𝑠

(4)

The normalized distributions of the syllable and

silence interval durations might be considered good

candidates to establish a differentiation protocol

between the behaviour of PD and HC participants in

mutual information terms(Cover and Thomas, 2006),

using the Jensen-Shannon Divergence (JSD). The

resulting sequences of syllable and silence interval

durations as d

(i) and d

(i) are approximated as

Kolmogorov-Smirnov distribution densities p

(i) and

(i) following Simard & L’Ecuyer, 2011. For each

probability density a distance to the average HC

subsets (p

msy

(i) and p

msi

(i) for males, and p

fsy

(i) and

fsi

(i) for females) was obtained using the JSD (see

Gómez et al., 2019 for a detailed description of the

JSD estimation).

3 RESULTS

The speech signal produced by each participant are

split into the corresponding diadochokinetic exercises

[…tatata…] and […pataka…]. Then they are

segmented into intervals with speech activity

(syllables) and with no speech activity (silences)

BIOSIGNALS 2021 - 14th International Conference on Bio-inspired Systems and Signal Processing

116

using the TKO and VUI indexes. One example of a

segmented speech sequence from a diadochokinetic

exercise can be seen in Figure 1.

Figure 1: Segmentation of the speech signal corresponding

to the diadochokinetic exercise […pataka…] from a male

HC participant (MC6): a) Speech segment under analysis;

) Results of TKO and VUI; c) Voice activity detection

(segmentation into syllable and silence durations).

The distributions from two diadochokinetic exercises

(…tatata… and …pataka…) uttered by the male and

female HC and PD participants are shown in Figure 2

to Figure 9.

Figure 2: Male Syllable sequence distributions fro

[…tatata…]. HC (left) and PD (right).

The distribution from the average HC densities is

shown in red in both plots for an easy comparison.

For the following figures the representation

conditions are the same as in figure 2.

Figure 3: Male Silence sequence distributions from

[…tatata…]. HC (left) and PD (right).

Figure 4: Female Syllable sequence distributions from

[…tatata…]. HC (left) and PD (right).

Figure 5: Female Silence sequence distributions fro

[…tatata…]. HC (left) and PD (right).

Performance of Monosyllabic vs Multisyllabic Diadochokinetic Exercises in Evaluating Parkinson’s Disease Hypokinetic Dysarthria from

Fluency Distributions

117

Figure 6: Male Syllable sequence distributions fro

[…pataka…]. HC (left) and PD (right).

Figure 7: Male Silence sequence distributions from

[…pataka…]. HC (left) and PD (right).

Figure 8: Female Syllable sequence distributions from

[…pataka…]. HC (left) and PD (right).

Figure 9: Female Silence sequence distributions fro

[…pataka…]. HC (left) and PD (right).

Complementary descriptions of the fluency sequence

of syllables and silences by the male and female

datasets (HC vs PD participants) are given in Table 2

to Table 9. The provided normality tests and the

Jensen-Shannon divergence are with respect to the

HC pool set. The subset gender, sequence and

exercise are highlighted in bold.

Table 2: Parametric description of the male Syllabic PD

and HC distributions, sequence […tatata…].

Code Ints Mean StdDev Skew Kurt

-vLil JSD

MP1 37 0.047 0.009 -1.018 4.430 0.029 0.597

MP2 38 0.067 0.008 -2.190 11.664 0.002 0.335

MP3 23 0.083 0.014 -1.571 7.584 0.103 0.277

MP4 26 0.058 0.006 0.405 2.183 0.244 0.462

MP5 22 0.085 0.011 -0.046 3.755 0.288 0.268

MP6 38 0.082 0.005 -0.039 2.404 0.500 0.326

MC1 42 0.075 0.008 0.115 4.243 0.276 0.242

MC2 38 0.083 0.009 0.343 4.953 0.427 0.277

MC3 54 0.062 0.009 1.142 5.317 0.079 0.406

MC4 43 0.076 0.018 -0.085 3.235 0.500 0.040

MC5 45 0.068 0.012 0.066 2.820 0.500 0.223

MC6 36 0.101 0.012 1.991 9.594 0.030 0.489

The first column from the left (code) gives each

participant’s code according to its gender (M: males,

F: females), health condition (C: HC, P: PD), and a

consecutive number from 1 to 6. The second column

(Ints) give the number of syllable or silence intervals

detected in each sample utterance. The third column

gives the value of the mean interval in seconds. The

fourth column gives its standard deviation in seconds.

The fifth column (Skew) gives the skewness

distribution, and the sixth (Kurt) one gives its

kurtosis. The seventh column (p-vLil) gives the p-

value of Lilliefors’ hypothesis test of the distribution

being normal (H0) on the confidence value of 0.05 (p-

BIOSIGNALS 2021 - 14th International Conference on Bio-inspired Systems and Signal Processing

118

value<0 means rejecting the normality hypothesis).

The eighth column (JDS) gives the Jensen-Shannon

distance of the sample distribution with respect to the

average pool of HC distributions. The data in bold

refer to the minimum and maximum of columns 2-4

and 8, and to the distributions rejecting H0 (5-7).

Table 3: Parametric description of the male Silence PD an

HC distributions, sequence […tatata…].

Code Ints Mean StdDev Skew Kurt p-vLil JSD

MP1 36 0.089 0.007 -0.463 2.543 0.147 0.267

MP2 37 0.067 0.008 -1.009 5.262 0.500 0.493

MP3 22 0.096 0.007 0.278 2.641 0.500 0.272

MP4 25 0.081 0.008 -1.430 5.102 0.104 0.331

MP5 21 0.095 0.007 0.342 2.937 0.500 0.278

MP6 37 0.099 0.006 -0.126 2.086 0.463 0.304

MC1 41 0.081 0.012 -0.376 3.006 0.352 0.241

MC2 37 0.069 0.012 0.253 2.516 0.500 0.432

MC3 53 0.105 0.007 -1.080 4.170 0.181 0.357

MC4 42 0.096 0.028 -0.246 1.932 0.168 0.247

MC5 44 0.098 0.012 -0.626 3.995 0.245 0.223

MC6 35 0.090 0.008 0.331 4.166 0.420 0.266

Table 4: Parametric description of the female Syllabic PD

and HC distributions, sequence […tatata…].

Code Ints Mean StdDev Skew Kurt p-vLil JSD

FP1 34 0.069 0.007 0.017 2.620 0.500 0.277

FP2 41 0.076 0.009 0.740 5.701 0.018 0.118

FP3 39 0.067 0.008 -0.598 5.224 0.221 0.325

FP4 28 0.074 0.006 -1.222 4.904 0.053 0.167

FP5 29 0.080 0.011 0.132 2.983 0.500 0.090

FP6 44 0.079 0.007 0.007 2.345 0.406 0.140

FC1 29 0.086 0.010 0.203 3.100 0.500 0.253

FC2 30 0.074 0.006 0.449 2.928 0.221 0.175

FC3 43 0.085 0.006 -0.265 2.399 0.500 0.267

FC4 37 0.070 0.005 0.002 2.302 0.500 0.275

FC5 45 0.069 0.007 1.352 6.635 0.002 0.316

FC6 30 0.086 0.015 -0.592 3.137 0.108 0.253

Table 5: Parametric description of the female Silence PD

and HC distributions, sequence […tatata…].

Code Ints Mean StdDev Skew Kurt p-vLil JSD

FP1 33 0.068 0.006 0.298 2.318 0.500 0.379

FP2 40 0.106 0.006 -0.242 6.016 0.473 0.515

FP3 38 0.080 0.007 -0.202 2.476 0.500 0.229

FP4 27 0.083 0.012 0.319 3.131 0.417 0.153

FP5 28 0.116 0.013 -0.343 2.339 0.364 0.525

FP6 43 0.070 0.006 -0.460 2.824 0.500 0.322

FC1 28 0.107 0.005 0.076 2.583 0.380 0.527

FC2 29 0.073 0.013 1.028 5.076 0.222 0.239

FC3 42 0.073 0.009 1.589 8.805 0.028 0.269

FC4 36 0.082 0.006 0.289 3.022 0.500 0.279

FC5 44 0.071 0.005 -0.196 2.552 0.269 0.310

FC6 29 0.094 0.011 -0.029 2.357 0.500 0.309

Table 6: Parametric description of the male Syllabic PD

and HC distributions, sequence […pataka…].

Code Ints Mean StdDev Skew Kurt p-vLil JSD

MP1 37 0.044 0.010 0.299 3.017 0.500 0.350

MP2 47 0.070 0.024 1.666 7.982 0.002 0.300

MP3 35 0.065 0.013 -0.270 2.748 0.277 0.286

MP4 52 0.058 0.015 0.014 2.034 0.275 0.117

MP5 35 0.065 0.012 -0.207 2.779 0.500 0.288

MP6 58 0.066 0.021 0.325 2.570 0.074 0.169

MC1 52 0.067 0.027 1.233 4.706 0.041 0.123

MC2 46 0.069 0.026 2.367 8.017 0.001 0.232

MC3 52 0.046 0.014 0.900 4.940 0.200 0.253

MC4 78 0.060 0.017 0.561 3.981 0.384 0.096

MC5 36 0.045 0.011 0.090 2.287 0.374 0.283

MC6 55 0.068 0.018 0.269 2.485 0.166 0.224

Table 7: Parametric description of the male Silence PD an

HC distributions, sequence […pataka…].

Code Ints Mean StdDev Skew Kurt p-vLil JSD

MP1 36 0.080 0.021 3.330 17.749 0.001 0.247

MP2 46 0.071 0.028 3.214 17.040 0.001 0.368

MP3 34 0.090 0.017 -0.705 2.644 0.001 0.233

MP4 51 0.062 0.007 -0.334 3.315 0.500 0.503

MP5 34 0.090 0.017 -0.703 2.645 0.001 0.235

MP6 57 0.083 0.028 4.117 23.219 0.001 0.259

MC1 51 0.073 0.029 1.388 5.587 0.007 0.278

MC2 43 0.058 0.018 0.184 3.022 0.233 0.440

MC3 51 0.091 0.018 5.351 35.660 0.001 0.280

MC4 76 0.074 0.021 0.133 3.488 0.114 0.177

MC5 35 0.098 0.011 0.423 2.487 0.420 0.335

MC6 54 0.096 0.043 5.962 40.379 0.001 0.223

Table 8: Parametric description of the female Syllabic PD

and HC distributions, sequence […pataka…].

Code Ints Mean StdDev Skew Kurt p-vLil JSD

FP1 45 0.065 0.020 0.134 3.594 0.266 0.371

FP2 68 0.056 0.017 0.454 2.661 0.277 0.129

FP3 37 0.056 0.021 0.912 3.981 0.278 0.138

FP4 33 0.072 0.011 -0.043 2.404 0.500 0.498

FP5 30 0.072 0.018 -0.919 4.466 0.247 0.489

FP6 61 0.053 0.020 0.228 1.881 0.004 0.245

FC1 38 0.047 0.008 -0.145 2.794 0.500 0.191

FC2 49 0.043 0.014 0.313 2.208 0.500 0.187

FC3 38 0.044 0.009 0.141 3.405 0.378 0.212

FC4 24 0.055 0.010 -0.622 2.704 0.342 0.231

FC5 52 0.057 0.016 0.569 2.527 0.088 0.132

FC6 35 0.058 0.022 0.357 2.846 0.500 0.263

An important estimate to be considered in analysing

the data presented in Table 2 to Table 9 is the average

of each sequence interval mean accordingly to

gender, condition, sequence, and exercise type, as

given in Table 10. The smaller and larger interval

duration averages in seconds are given in bold.

Performance of Monosyllabic vs Multisyllabic Diadochokinetic Exercises in Evaluating Parkinson’s Disease Hypokinetic Dysarthria from

Fluency Distributions

119

Table 9: Parametric description of the female Silence PD

and HC distributions, sequence […pataka…].

Code Ints Mean StdDev Skew Kurt p-vLil JSD

FP1 44 0.088 0.035 2.854 12.121 0.001 0.146

FP2 67 0.095 0.052 2.774 11.722 0.001 0.120

FP3 37 0.101 0.027 1.682 8.820 0.002 0.424

FP4 32 0.065 0.017 0.370 2.048 0.046 0.300

FP5 29 0.127 0.031 1.217 4.653 0.056 0.588

FP6 60 0.075 0.040 3.923 21.516 0.001 0.309

FC1 37 0.086 0.014 -0.060 1.823 0.151 0.231

FC2 47 0.081 0.026 2.125 7.966 0.001 0.094

FC3 37 0.061 0.008 -0.142 4.521 0.500 0.419

FC4 23 0.074 0.008 -0.536 3.211 0.500 0.204

FC5 51 0.077 0.022 3.490 21.763 0.001 0.116

FC6 34 0.087 0.020 3.084 15.917 0.001 0.235

Table 10: Interval averages in ms by category (out of 1964

syllabic and 1913 silence intervals).

#Col Gen. Cond. Seq. Exer. Averages (ms)

1 M HC Syl. tatata 0.077

2 M HC Syl.

ataka 0.059

3 M HC Sil. tatata 0.090

4 M HC Sil.

ataka 0.082

5 M PD S

l. tatata 0.070

6 M PD Syl.

ataka 0.061

7 M PD Sil. tatata 0.088

8 M PD Sil.

ataka 0.079

9 F HC S

l. tatata 0.078

10 F HC S

ataka 0.051

11 F HC Sil. tatata 0.083

12 F HC Sil.

ataka 0.078

13 F PD Syl. tatata 0.074

14 F PD S

ataka 0.062

15 F PD Sil. tatata 0.087

16 F PD Sil.

ataka 0.092

Table 11: Number of samples not rejecting Lilliefors'

normality hypothesis test (out of 48).

Category

Type #Samples Percent (%)

By Exercise tatata 42 87.5

pataka 28 58.3

By Sequence Sil. 31 64.6

Syl. 39 81.3

By Gender F 36 75.0

M 34 70.8

By Condition HC 37 77.1

PD 33 68.8

The number of sample utterances not rejecting H0

according to the categories of exercise, sequence,

gender, and condition is given in Table 11.

A summary of the regularity of each subset in

terms of number of distributions not rejecting the null

hypothesis, and their comparisons with respect to the

HC averages is given in Table 12.

Table 12: Results of comparing the JSD and StdDev wit

the HC subset averages. #Norm: number of samples no

rejecting the normality hypothesis. #>AvJSD: number o

samples over the average JSD of the HC subset. #>AvStd:

Idem over the average StdDev of the HC subset.

# Exer. Seq. Gen. Cond.#Norm #>AvJSD #>AvStd

1 tatata Sil. F PD 6 3 2

2 tatata Sil. F HC 5 1 3

3 tatata Syl. F PD 5 2 3

4 tatata Syl. F HC 5 3 2

5 tatata Sil. M PD 6 3 0

6 tatata Sil. M HC 6 2 1

7 tatata Syl. M PD 4 4 1

8 tatata Syl. M HC 5 2 3

9 pataka Sil. F PD 1 4 6

10 pataka Sil. F HC 3 3 3

11 pataka Syl. F PD 5 4 5

12 pataka Syl. F HC 6 3 3

13 pataka Sil. M PD 1 2 2

14 pataka Sil. M HC 3 2 2

15 pataka Syl. M PD 5 4 2

16 pataka Syl. M HC 4 4 2

4 DISCUSSION

The review by exercise, condition, gender and

sequence is explained in what follows. It may be seen

in Figure 2, corresponding to the monosyllabic

repetition […tatata…], that contrary to expectations,

the HC distributions are more spread and over their

average distribution (in red) than the PD counterparts,

which are slender (concentrated). This observation

may be related with the effects of repetitive regular

cue rates in stabilizing the movements in PD patients

(Harrison, Horin and Earhart, 2019). The sequence

distribution of silence intervals in Figure 3, shows a

similar behaviour, the HC distributions being more

widespread than that of PD participants. Interestingly,

the sequence distributions of syllable intervals in

Figure 4 by female participants shows little dispersion

and good alignment with the average of HC

distributions. The situation is completely different

regarding the sequence distributions of silence

intervals shown in Figure 5, which shows a much less

organized pattern of more widespread distributions.

When examining the results of the multisyllabic

exercise […pataka…] the distributions become more

widespread, and many of them exhibit multimodal

behaviour, something not observed in the

monosyllabic exercise. This behaviour may be seen

in Figure 6 and Figure 7, corresponding to syllable

and silence sequence distributions from the male HC

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and PD datasets, although in this last figure the PD

distributions are more regularly aligned than the HC

counterpart. Figure 8 shows the syllable sequence

distributions from females, and in this case the HC

subset is more regular than the PD subset. This

behaviour is also evident in Figure 9, where the PD

subset is much less organized than the HC. This could

be a consequence of the less regularity observed in

the repetitive pattern when multisyllabic repetition is

required.

The visual information provided by figures is

complemented with the tabulated parameters from

each distribution given in Table 2 to Table 9.

Additional relevant information is provided, as the

number of intervals produced by each speaker, which

depends on different factors, respiratory capacity

among them. Table 2 shows that the smallest and

largest number of intervals, means and dispersion

correspond to different members of the PD and HC

subsets, respectively. The largest JSD corresponds to

a PD participant, and the smallest to an HC one. Two

distributions reject the null hypothesis in the PD

subset (MP1, which shows the smallest syllabic

interval mean, and the largest JSD to the HC average).

The examination of Table 3 shows that the smallest

and largest number of intervals and means correspond

to a member of the PD and the HC, respectively. The

largest JSD is from a PD participant, and the smallest

one from an HC participant. In this case, none of the

distributions reject the null hypothesis. Table 4 shows

a similar behaviour regarding the number of intervals

and their means, but the situation is quite different as

far as JSD is concerned. In this last case, the smallest

and largest distances are found in the PD dataset. The

number of distributions rejecting the null hypothesis

is two, one in the PD dataset and one in the HC

dataset. The situation reported in Table 5 shows that

the smallest number of intervals is produced by a

member of the PD subset, and the largest by an HC

member, but the smallest and largest interval means

correspond to members from the PD subset. Contrary

to what may have been expected, the largest JSD

corresponds to a member of the HC subset, and the

shortest JSD to a member of the PD subset (FC1). The

reason has to be found in the separation of the silence

sequence distribution of FC1 with respect to the HC

subset average. The number of distributions rejecting

H0 is two, one from the PD subset, and one from the

HC subset. The multisyllabic exercises reflect a more

irregular situation. Table 6 shows that the smallest

and largest number of intervals correspond to the PD

and HC subsets, respectively. The smallest and

largest means are both from the PD subset. The

smallest and largest standard deviations are from the

PD and HC subsets, respectively. The largest and

smallest JSDs are from the PD and HC subsets,

respectively. The number of distributions rejecting

the null hypothesis is three, one from the PD and two

from the HC subsets. But the situation becomes much

more irregular when examining the distributions of

the silence intervals given in Table 7. Whereas the

smallest and largest number of intervals correspond

to the PD and HC subsets, respectively, the smallest

and largest means are both from the HC subset. The

largest and smallest JSDs are from the PD and HC

subsets, as it could be expected, but the number of

distributions rejecting the null hypothesis is eight,

five from the PD subset and three from the HC subset.

Would this mean that males have more problems in

separating syllables in a regular way when facing a

multisyllabic exercise? Table 8 shows a less irregular

situation, although the largest and smallest number of

intervals are produced by members of the PD subset,

whereas the largest and smallest means correspond to

two members of the PD subset and a member of the

HC subset, respectively. The largest and smallest

JSDs are produced by two distributions of the PD

subset. Only one distribution from the PD dataset

rejects the null hypothesis. Finally, the situation

described in Figure 9 shows again an irregular

behaviour as far as the separation of syllables by

females facing multisyllabic repetitions is concerned.

Contrary to what could be expected, the largest and

smallest numbers of intervals were produced by a PD

subset member and by an HC one, respectively. The

largest and smallest silence interval mean duration

were produced by two members of the PD subset. The

largest and smallest JSDs corresponded to a PD

subset member and to an HC member, respectively.

But the most remarkable observation is that the

number of distributions rejecting the null hypothesis

is again eight, five from the PD subset, and three from

the HC subset, showing a striking resemblance with

the male cases described in Table 7.

The averages of all speakers’ mean interval

duration are summarized Table 10 by gender,

condition, duration sequence, and exercise. It may be

observed that in all cases but one, the average

duration of syllables and silences from the

monosyllabic exercise are longer than those from the

multisyllabic exercise, with the exception of the

silence sequences produced by female PD

participants (rows 15 vs 16). The duration of syllables

is shorter than the duration of silences comparing by

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gender and condition. Compare for instance, row 1

against row 3 (0.077 ms vs 0.090 ms), and row 2

against row 4 (0.059 ms vs 0.082 ms). The results

given in 0 help in explaining the regular behaviour of

the data derived from the two diadochokinetic

exercises, in terms of the statistical characteristics of

their interval distributions. The largest number of

sequences with distributions not rejecting the

normality hypothesis is attributed to the sequence

[…tatata…], whereas […pataka…] produced less

distributions fulfilling the same condition. This fact

explains the difficulty of HC and PD participants in

producing regular intervals when facing a relatively

more complicate exercise. Interpreting the normality

behaviour of distributions as a hallmark of regularity,

the most regular subsets correspond to silence

intervals from the monosyllabic exercise. The

multisyllabic exercise produced more normal-like

distributions for syllable than for silence intervals.

Therefore normality tests to differentiate PD from HC

behaviour might work better with the multisyllabic

exercise than with the monosyllabic one. In this same

respect, assuming that the JSD would be used as a

feature, the most efficient exercises would correspond

to the ones marked in bold in column #>AvJSD

(number of PD participants producing a JSD larger

than the average of the HC subset, compared to HC

participants, as given by rows 7 vs 8, 9 vs 10 and 11

vs 12, where the number of participants from the PD

subset showing larger JSDs are well over the number

of HC participants in the same case. Proceeding

similarly with respect to standard deviations, as given

in column #>AvStd, the most differentiating

conditions are the ones given by row 9 vs 10 and 11

vs 12. Therefore, the best candidates to be checked in

a further study would be the syllabic interval

durations from the monosyllabic exercise in the case

of males, and the syllabic and silence interval

durations from the multisyllabic exercise in the case

of females.

It might be inferred from what has been exposed

that fast purely repetitive exercises provide a timely

cadence to PD participants which help them in

successful fast repetition. On the contrary, mixed-

syllable exercises require a conscious control of

syllable sequence repetition (sequence planning and

executing), presenting an added difficulty for HC and

PD participants, but HC participants seem to utter the

multisyllabic sequence faster. The added difficulty

found in the multisyllabic exercise may be related to

the extra difficulty found in executing the neuromotor

changes implied in the articulation from bilabial [pa]

(orofacial) to apical-alveolar [ta] (lingual) and dorsal-

velar [ka] (lingual-pharyngeal), involving quite

different muscular systems. These facts which would

explain the complete differential behaviour in

exercise planning and execution, from the purely

repetitive to the alternating planning, and the control

of complete different neuromotor pathways and units.

The main weakness of this study is its non-

conclusive character, as the data sample examined is

quite low, but it has an exploratory value to initiate a

larger scale study. The slightly difference in the age

range of HC and PD participants might introduce

some bias in the comparisons as well, this fact

needing a further study as highlighted by Gómez et

al., 2019.

5 CONCLUSIONS

The study of the potential capabilities of repetitive

spoken diadochokinetic exercises might derive

important benefits to plan speech databases and

machine learning methods to characterize PD. The

main findings in this study are the following:

 Multisyllabic exercises appear to have more

discriminatory power compared to monosyllabic

exercises when it comes to assessing PD vs HC..

 PD participants would produce good and regular

syllable and inter-syllable intervals, and at a faster

repetition rate when monosyllabic exercises are

concerned. This fact should be carefully

considered when analysing the differentiation

capability of monosyllabic exercises.

 On the contrary, PD participants could face more

difficulties when multisyllabic exercises are used,

therefore these exercises should be prioritized

when used combined with other diadochokinetic

exercises in test design and analysis.

 The statistical behaviour of syllabic and inter-

syllabic interval sequence durations of

multisyllabic exercises deviates from normality,

therefore the statistical evaluation of these tests

must stand on non-parametric methods.

 JSDs seem to be sufficiently sensitive to be used

in establishing standard syllable and silence

interval durations from distance estimations

among duration distributions.

This last aspect was not included per se as an

objective of the study, however the methodology we

have used does not rely on expensive high quality

equipment that most studies in the field rely on. This

concept is very much aligned with the spirit of the

Parkinson’s Voice Initiative (PVI), see Arora, Bahai-

Ravary and Tsanas, 2019, and the exploration of work

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trying to facilitate low-cost, robust assessment of PD

using readily available means. In this sense we are

working on extending these findings on the PVI

database.

ACKNOWLEDGEMENTS

This work is being funded by grants TEC2016-

77791-C4-4-R from the Government of Spain, and

CENIE_TECA-PARK_55_02 INTERREG V-A

Spain – Portugal (POCTEP).

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