Parkinson’s Disease Glottal Flow Characterization: Phonation

Features vs Amplitude Distributions

Agustín Álvarez

, Andrés Gómez

, Daniel Palacios

, Jiri Mekyska

Athanasios Tsanas

, Pedro Gómez

and Rafael Martínez

Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de Madrid,

Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain

Usher Institute, The University of Edinburgh, Edinburgh Bioquarter, 9 Little France Road, Edinburgh, EH16 4UX, U.K.

Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Calle Tulipán, s/n, 28933 Móstoles,

Madrid, Spain

Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic

Keywords: Neuromotor Disease Phonation, Glottal Signature, Parkinson’s Disease, Aging Voice.

Abstract: The study of speech and voice in people diagnosed with a neurodegenerative disorder for the purposes of

detection and monitoring has known a very relevant push forward in these last years, but it is far from being

completed. One of the main concerns nowadays is that once the deterioration of speech and phonation quality

has been informed by machine learning relying upon clinical expertise, there is insufficient evidence to resolve

if quality deterioration may come from organic causes, neuromotor degeneration or simply from aging. The

present work is part of a more ambitious plan to shed light on this problem by resorting to a theoretical

modelling of glottal signals under the main known causes affecting phonation quality, which are closure

deficits during the phonation cycle. These deficits may be due to anatomical, organic pathologic or

neuromotor reasons. Simulation examples explaining them in the glottal excitation signals are given and

contrasted with real examples. Finally, relevant scores from an experimental separation of Parkinson Disease

phonation samples from 24 male and 24 female subjects against aging 24 male and 24 female controls on the

same age taken from a male-female balanced dataset confronted to a normative subset of 24 male and 24

female speakers are presented to exemplify an analysis study deepening into this problem. Although

classification accuracy scores as high as 99.69 and 99.59 were attained in 10-fold cross-validation using an

SVM classifier, there is still the impression that co-morbidity and aging effects are not well taken into account,

requiring a further semantic study on the features behind the discrimination scores obtained.

1 INTRODUCTION

Parkinson’s Disease (PD) is a neurodegenerative

disorder second in prevalence to Alzheimer’s Disease

(Dorsey et al., 2007). Its origin is mainly caused by

the lack of a specific neurotransmitter known as

dopamine in midbrain (Dauer & Przedboski, 2003),

resulting in important neuromotor deterioration

affecting body movement (Jankovic, 2008). Other

comorbidities associated to PD are e. g. depression,

and cognitive decay (Reijnders et al., 2008). Since the

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early work of Dr. James Parkinson (Parkinson, 1817)

describing observable neuromotor alterations in

patients of shaking palsy, it is a well-established fact

that PD produces important disorders in speech and

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

2017). Basically, 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). Therefore

Álvarez, A., Gómez, A., Palacios, D., Mekyska, J., Tsanas, A., Gómez, P. and Martínez, R.

Parkinson’s Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions.

DOI: 10.5220/0009189403590368

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS, pages 359-368

ISBN: 978-989-758-398-8; ISSN: 2184-4305

359

speech analysis may become a non-invasive and cost-

efficient tool to characterize and monitor PD in the

sense that there is “compelling evidence to suggest

that speech can help quantify not only motor symptoms

... but generalized diverse symptoms in PD” (Tsanas,

2012). The first aim of this study is answering in which

way neuromotor deterioration affecting the larynx may

result in phonation alterations, and which are the

observable correlates to be expected. The second aim

of the study is to assess in which extent neuromuscular

alterations can help in differentiating PD patients

against age-paired Healthy Controls (HC) in reference

to the phonation of normative subjects (NS), see

Gómez et al. (2019). The paper is organized as follows:

in Section 2 the main glottal pattern deteriorations

expected from altered phonation are described and a

phonation model is presented to simulate these altered

patterns; in Section 3 the phonation databases and the

classification methods used are being described; in

section 4 results are presented and discussed, as well as

the semantic characteristics of the main features

responsible for categorical classification based on

phonation; section 5 summarizes the main conclusions

and findings.

Figure 1: The human phonation system. a) Idealized axial

representation. b) Simplified cylindrical representation. c)

Electromechanical equivalent model, where G

is the glottal

aperture (time-varying electromechanical equivalent

conductance, thin red line) u

is the glottal flow

(electromechanical equivalent current, thick purple line), t

is the time. The model explains how the injection of flow u

is transformed in a difference of pressure between the

supraglottal (p

) and subglottal (p

) sides of the moving

vocal folds (represented by the red bullets) and transformed

into a glottal excitation by the inertial effects of air in the

vocal tract (L

). C

explains the reactive elastic behavior of

lungs and bronchi. See the explanation below.

2 PHONATION ALTERATIONS

Voiced speech is the result of the continuous opening

and closing of the vocal folds, specific muscle bands

and ligaments found in the neuromuscular and

cartilaginous structure of the larynx (Titze, 1994).

This section is devoted to explain certain phonation

defects resulting from the imperfect closure of the

vocal folds during the phonation cycle, which may be

related to different causes, organic pathology,

neuromotor instability or aging, among them. The

Human Phonation System (HPS) is part of the

respiratory organs, and as such, it is integrated by the

respiratory system in itself, the larynx, and the oro-

naso-pharyngeal cavities, as seen in 0. The HPS

comprises different flow/pressure propagating

structures (lungs, bronchi, trachea, larynx, pharynx,

nasopharynx and oral and nasal cavities), ending at

the lips. In the present work it must be taken into

account that:

• The system of cavities may be seen on their

equivalent transversal section as a volume closed

by an ideal revolving surface of varying section

along a single axial line (x axis, see 0.a). The

axial line in the sagittal plane may be

straightened to an axis normal to the mouth

radiation plane (medial). The distances along this

rectified axis will be denoted by x. The origin x=0

will be taken at the lips, pointing to the glottis

(space separating both vocal folds at x=x

• This structure may be represented by a chain of

cylindrical structures for the lungs, bronchi,

trachea, larynx and vocal tract

(0.b).Electromechanical equivalence convention

used in the HPS assumes that pressures are

equivalent to voltages and flows to currents in an

electric circuit. With this convention in mind the

equivalent cylindrical structure may be

represented by its electromechanical equivalent

given in 0.c, integrated by a flow injector

(diaphragm and lungs acting as a piston and

chamber) represented by a current source u

(glottal lung flow) and an elastic compliance C

a larynx passage represented by moving bullets

(vocal folds) which open and close following

transversal muscle forces and Bernoulli’s effect,

represented by a time-variable conductance G

(t)

and the vocal tract tube, represented by an inertial

reactance L

through which a current u

(glottal

flow) will be pushed to the lips. The pressure

build-up at the subglottal side of the glottis will

be represented by a voltage p

(subglottal

pressure), whereas the voltage p

(supraglottal

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360

pressure) will appear at the other side of the

glottis. The pressure at the lips will be the

atmospheric reference pressure p

. This is the

basis of Rothenberg’s model of phonation

(Rothenberg, 1973), which will be used in this

study to reproduce basic phonation defects.

• Only oral phonations will be studied here, in

order for the HPS to be modeled as a single tube

with no lateral derivations. This condition will

exclude the production of phonations related

with [m, n, ɲ] and nasalized vowels ([ῦ], with

open velo-pharyngeal switch).

The idealized HPS will assume that plane wave

propagation is to be considered along the transversal

section of the equivalent cylindrical model S(x). It

will be possible to represent the HPS three main

subsystems described by the following models:

• The respiratory subsystem, comprising the

diaphragm, lungs, bronchi and trachea, where C

is the mechanical compliance of the respiratory

cavities, and u

=u(x=x

,t) and u

=u(x=x

,t) are the

airflows at the subglottal (x=x

) and supraglottal

(x=x

) sides of the vocal folds given by the

following relationship:



(





,

)

=





(





,

)



+



,



(1)

• The glottal subsystem, comprising vocal fold

biomechanics will be represented by its

electromechanical equivalent conductance G

which under the pressure difference between the

subglottal p

=p(x

,t) and supraglottal p

=p(x

,t)

sides of the vocal folds induces a flow of air as:





,=



()(

(





,

)

−(



,)) (2)

• The Oro-Naso-Pharyngeal Tract (ONPT), which

may be represented by a tube of uniform section

and inertial parameter L

, and the contour

conditions at x=0 (open space) and x=x

follows:





,=







,



; 

(

0, 

)

(3)

The HPS considers that voice is produced by the

vibrations of the vocal folds, represented by red

bullets moving transversally to the axial line x, which

result in strong pressure changes in the supraglottal

side of the vocal folds. This pressure signal p

=p(x

,t)

is known as the glottal source. Indeed, the movement

of the vocal folds is much more complex, as it has to

be observed in the transversal section of the glottis, as

seen in Figure 2.a-f. The idealized model used in this

study assumes that the transversal motion of the vocal

folds is represented by the average space left by both

vocal folds in their medial section (midpoint between

the lower and upper bands on the vertical axis of any

of the templates a-f in Figure 2).

Figure 2: Glottal aperture (Top): a) Inspiration or

Expiration; b) Full closure; c) Phonation open phase; d)

incomplete permanent gap defect; e) unilateral vocal fold

paresis; f) medial contact gap defect; (Bottom): glottal

aperture function G

(t) corresponding to permanent and

temporary contact gap defects.

In the upper template six different patterns of the

vocal fold closure are seen, corresponding to:

a) Inspiration or Expiration. Both vocal folds are

separate and allowing the flow to and from lungs.

b) Full closure. The vocal folds are brought close

together by their attachments to the aritenoid

cartilages (black diamonds). No glottal space is

left. This may happen when breath is held or

during the contact phase of the phonation cycle.

c) Phonation open phase. The vocal folds are

brought together but air pressure from lungs can

Parkinson’s Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions

361

separate them momentarily when an air puff is

released (glottal pulse).

d) Permanent gap defect. Both vocal folds cannot be

brought together to a complete closure, and some

glottal space is left in the aritenoid side. This gap

defect is usually of anatomical nature.

e) Asymmetric contact defect. One of the vocal

folds is not reacting to neuromotor activation and

cannot be taken to its medial position, therefore

a complete closure cannot be reached. It is

usually due to physiological paralysis of one of

the vocal folds, or to a lesion of laryngeal nerves.

f) Contact gap defect. Both vocal folds cannot be

taken to a complete closure during the contact

phase due to some organic lesion on their

structure, as nodules, polyps or cysts, among

others. If the lesion affects the medial part of the

vocal folds two spaces are left on the aritenoid

(bottom) or thyroid (top) sides in a shape of an

hourglass. This defect appears as a temporary

undue opening during the contact phase, during

which an improper escape of flow is produced,

as seen in the lower template of Figure 2, where

the time pattern of a hypothetical aperture

function (Gg) is represented, in which a

permanent escape of air is present due to a

permanent defect of amplitude 0.2 relative to the

maximum flow (supposed 1.0), besides a

temporary contact defect of relative amplitude

0.15 between 1.5 and 3.5 ms. The main aperture

(proper flow escape) is observed between 5.5 and

10 ms (assumed to be cyclic). The time axis is

referenced to the Maximum Flow Declination

Ratio (MFDR), therefore part of the closing

phase of the glottal aperture has assigned

negative time values.

In general, gaps and contact defects lead to the

creation of vortices and noise turbulence, which are

effects out of the scope of the present study. Different

patterns of the glottal flow and source are shown in

the next plots from Figure 3 to Figure 6, as a response

of different glottal aperture functions when

synthesized by Rothenberg’s model. All of them

show the amplitude-normalized glottal flow (a), its

amplitude distribution from a 100-bin normalized

histogram (b) the amplitude-normalized glottal

source (c) and its amplitude distribution (d). The

glottal source in c) is synchronized with the strong

decay resulting from the closing of the glottis aligned

with the origin and end of the glottal cycle (MFDR),

as it is produced by the sharp pressure decay when the

flow is decreasing at its fastest rate in a), according to

(3). The glottal source cycle is initiated with a fast

pressure raise to 0 (resting state at atmospheric

pressure), the flow in a) being zero (contact or closed

phase). At t=5 ms the aperture function in Figure 2.f)

starts opening, which results in a raise in flow (Figure

3.a) and a build-up in glottal pressure (Figure 3.c).

The maximum in the glottal pressure (source) is

aligned with the maximum slope in the glottal flow.

When the glottal flow (a) reaches a maximum value,

the glottal source crosses the zero line and becomes

negative (c). At the point that the glottal flow reaches

its minimum negative slope (a) the glottal source (c)

reaches its minimum (MFDR). This evolution of the

glottal source is known as the Liljencrants-Fant cycle

or LF pattern (Fant and Liljencrants, 1985).

Figure 3: Glottal flow and source response of Rothenberg’s

Model to a normative aperture function (no permanent or

gap defects): a) Normalized glottal flow; b) 100-bin

amplitude histogram of the glottal flow; c) Normalized

glottal source under the same conditions; d) 100-bin

amplitude histogram of the glottal source.

The glottal source in c) is synchronized with the

strong decay resulting from the closing of the glottis

aligned with the origin and end of the glottal cycle

(MFDR), as it is produced by the sharp pressure

decay when the flow is decreasing at its fastest rate in

a), according to (3). The glottal source cycle is

initiated with a fast pressure raise to 0 (resting state at

atmospheric pressure), the flow in a) being zero

(contact or closed phase). At t=5 ms the aperture

function in Figure 2.f) starts opening, which results in

a raise in flow (Figure 3.a) and a build-up in glottal

pressure (Figure 3.c). The maximum in the glottal

pressure (source) is aligned with the maximum slope

in the glottal flow. When the glottal flow (a) reaches

a maximum value, the glottal source crosses the zero

line and becomes negative (c). At the point that the

glottal flow reaches its minimum negative slope (a)

the glottal source (c) reaches its minimum (MFDR).

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362

This evolution of the glottal source is known as the

Liljencrants-Fant cycle or LF pattern (Fant and

Liljencrants, 1985). In Figure 4 a case of permanent

gap defect is simulated. The vocal folds leave a

permanent space for airflow, usually by the aritenoid

zone, as depicted in Figure 2.d.

Figure 4: Glottal flow and source response of Rothenberg’s

Model to an anomalous aperture function (permanent gap).

a) Normalized glottal flow. b) 100-bin amplitude histogram

of the glottal flow. c) Normalized glottal source under the

same conditions. d) 100-bin amplitude histogram of the

glottal source.

It may be seen that after reaching a non-zero

minimum the glottal flow (a) experiences an

increment in time during what should be the contact

phase, to the point where it burst-up (open phase) to

start a decay. The glottal source (c) does not recover

to the resting state, but it overpasses it, and a small

and almost steady overpressure is observed till the

opening burst-up and posterior decay to its MFDR.

The glottal flow amplitude distribution (b) suffers a

relevant distortion, most of the accumulation at lower

amplitudes being displaced to mid amplitudes. The

glottal source amplitude distribution (d) does not

suffer important changes (a widening and a small

jump of the expected spike at the resting position).

In Figure 5 a case of temporary contact defect is

simulated. A puff of flow is produced during the

contact phase, similar to the one represented in Figure

2.f, but with no permanent gap. It may be seen that

the temporary contact defect appearing between 1.5

and 3.5 ms produces a reduced replica of the main

glottal source LF pattern (c), which may be

considered a wavelet of the main cycle (Mallat,

1998). Thus, wavelet description techniques have

been used in characterizing it (Gómez et al., 2013a).

The glottal flow and source amplitude distributions

are not strongly affected with respect to the normative

case (Figure 3).

Figure 5: Glottal flow and source response of Rothenberg’s

Model to an anomalous aperture function (temporary

contact gap). a) Normalized glottal flow. b) 100-bin

amplitude histogram of the glottal flow. c) Normalized

glottal source under the same conditions. d) 100-bin

amplitude histogram of the glottal source.

In Figure 6 a case of joint permanent and temporary

contact defects is simulated. A puff of flow is

produced during the contact phase, similar to the one

represented in Figure 5, and a steady flow escape as

in Figure 4 are present simultaneously, as given in

Figure 2.f.

Figure 6: Glottal flow and source response of Rothenberg’s

Model to an anomalous aperture function (permanent and

temporary contact gaps). a) Normalized glottal flow. b)

100-bin glottal flow amplitude histogram c) Normalized

glottal source under the same conditions. d) 100-bin glottal

source amplitude histogram.

The simulation results show a combined behavior of

the cases presented in Figure 4 and Figure 5, a steady

increment in the flow (a) as a consequence of the

permanent defect, and a wavelet replica of the main

glottal cycle (c) as a consequence of the contact

defect. Glottal flow amplitude distribution

experiments a larger distortion due to permanent

defect than to contact defect. The question now is to

Parkinson’s Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions

363

which extent these synthesized simulations can

represent real phonation defects. For such, a real

glottal flow and source cycle and its associated

amplitude distribution, resulting from the analysis of

a segment of vowel [a:] by a male speaker with

anomalous phonation are given as an example in 0

(Top four templates), whereas in the bottom four

templates synthetic glottal flow and source patterns

produced by Rothenberg’s Model are shown.

Figure 7: Top: Glottal flow and source estimated from a

pathological phonation of [a:] by a male speaker: a) Glottal

flow; b) Glottal flow distribution; c) Glottal source; d)

Glottal source distribution. Bottom: Glottal flow and source

synthesized by Rothenberg’s Model from an anomalous

aperture (permanent and contact gaps): e) Normalized

glottal flow; f) 100-bin amplitude histogram of the glottal

flow; g) Normalized glottal source; h) 100-bin amplitude

histogram of the glottal source. Model settings: Contact

defect t

=3.2 ms; Amplitude of contact defect A

=35%

max aperture; Phonation period t

=8 ms; Open phase

=3.6 ms.

It may be seen that the synthetic glottal signals

replicate the anomalous contact behavior of the real

ones (obtained by vocal tract inversion) in amplitude

and time. The similarity between the real and

synthetic amplitude normalized histograms is

evident. This example gives support to the use of

amplitude distributions in pattern matching tasks

related with pathology detection and monitoring from

phonation samples (see Kreiman, 2012).

3 MATERIALS AND METHODS

Following this reasoning, the possibility of using

phonation stability features in maintained open

vowels as [a:] has been evaluated in characterizing

neurologic PD patient phonation from that of

normative and healthy controls within the same age

range. The validation of phonation and contact

defects in the separation of pathological PD and aging

voice samples from normative ones has been carried

out using features from PD patients’ phonations vs

healthy controls, and normative speakers. The data

used were extracted from vowel utterances of [a:]

from 24 male and 24 female PD patients randomly

selected from male and female databases (Mekyska et

al., 2015) within an age range of 67.5±7.7 and

67.4±9.1 years (respectively) processed to produce a

PD database (MPD from male subjects, and FPD for

female ones). The average time interval since their

first diagnostic to the recording instant was of

8.17±4.21 years and 6.46±3.59 years respectively.

Patients were recorded in ON state about 2 hours after

medication intake. Similar utterances from another

set of 24 male and 24 female control subjects

randomly selected within an age of 61.3±9.3 and

65.1±8.9 years old respectively from the same

database were also processed and stored in a healthy

control database (MHC from male subjects and FHC

from female ones). The database (PARCZ) was

collected at St. Anne’s University Hospital in Brno

(Czech Republic), including demographic and

clinical information from each patient as gender, age,

time since first diagnosis, scores of the Unified

Parkinson’s Disease Rating Scale part III (UPDRS-

III: motor examination), medication and other non-

motor evaluations. Recordings were sampled at 48

kHz with 16-bit resolution. All patients signed an

informed consent form that was approved by the local

ethics committee. This partial study was also

approved by the Ethical Committee of Universidad

Politécnica de Madrid. Similarly, 24 male and 24

female subjects were randomly selected from a

normative database recorded at Hospital Gregorio

Marañón, of Madrid (HUGM), Spain, within an age

range of 42.3±11.2 and 37.3±11.7 (years)

respectively to serve as the normative male and

female datasets. In this case the recordings were

sampled at 44.1 kHz with 16-bit resolution. The

normative subsets are (Male Normative Set) and FNS

SERPICO 2020 - Special Session on Mining Self-reported Outcome Measures, Clinical Assessments, and Non-invasive Sensor Data

Towards Facilitating Diagnosis, Longitudinal Monitoring, and Treatment

364

(Female Normative Set). Although the databases

PARCZ and the HUGM have been recorded at

different sampling rates, down-sampling to 16 kHz

under channel equalization conditions grant similar

signal quality. Their main difference is on the

phonation characteristics, in PARCZ subjects

produced vowels no long than 2 s, whereas in HUGM

speakers could sustain vowels for 5 s and more. The

analysis has been conducted on 300 ms segments

taken from the middle of the vowel nucleus for best

results, down-sampling them to 16 kH to grant good

spectral contents at a reasonable computational cost.

The features used in the study are of two types:

average and standard deviation of jitter, shimmer,

noise-harmonic ratio, biomechanical parameters and

tremor estimations on one side (Gómez et al., 2017),

and glottal flow amplitude distributions as described

in section 2 on the other side. Feature extraction was

carried out according to the protocol described below:

• Recordings of [a:] from MNS, FNS, MHC, FHC,

MPD and FPD are down-sampled to 16 kHz.

• The ONPT transfer function is evaluated by a 20-

pole adaptive inverse lattice-ladder filter (Deller,

Proakis and Hansen, 1993). This transfer function

is removed from the spectral contents of the

speech signal (inversion). A complete description

of the adaptive inversion filter details can be

found in Gómez et al. (2009).

• The inverse filtering residuals are integrated twice

to give the glottal source and flow (p(x

,t), u(x

,t)).

• A normalized 50-bin amplitude histogram of

u(x

,t) is estimated for each subject sample.

• The histograms are used to estimate the

Amplitude Distributions (AD) by Kolmogorov-

Smirnov approximations (Webb, 2002).

• An average amplitude distribution is estimated for

each subset: avADMNS, avADFNS, avADMHC,

avADFHC, avADMPD and avADFPD.

• The glottal cycle-synchronous average (av)

estimates of the standard features (SF) as jitter,

shimmer, noise-harmonic ratio, vocal fold mass

and stiffness, their one-cycle unbalances, the three

termor band amplitudes, and their standard

deviations (sd) are also estimated as: avSFMNS,

avSFFNS, avSFMHC, avSFFHC, avSFMPD and

sdSFFPD and sdSFMNS, sdSFFNS, sdSFMHC,

sdSFFHC, sdSFMPD and sdSFFPD.

The classification methodology used is based on a

standard Support Vector Machine classifier

according to the following protocol:

• Feature selection is applied to each feature set

(FSA and FSB) using ReliefF (Kononenko et al.,

1997, Robnik-Šikonja and Kononenko, 2003),

with a number of neighbors varying between 1

and 50. As a result, 50 different arrangements in

ranking order for the total number of features

considered (144 from feature averages and

standard deviations and 100 from amplitude

distributions) are produced. Features are selected

in subsets of N features (between 15 and 120) in

order of ranking, from the highest to the lowest,

accordingly to the ranking provided by ReliefF.

• Each feature subset is the input to a Support

Vector Machine (SVM) with a Gaussian radial

basis function (RBF) kernel (Cortes and Vapnik,

1995) following the implementation given in

(Chang and Lin, 2011). In the present work SVMs

were used as an alternative to generate

classification results using cross-validation of all

the datasets distributed in 10 groups (10-fold

cross-validation). The process described is carried

on all the combinations of SVM grid space

parameters (C, γ) given as C=[2

-3

, 2

-2

,..., 2

] and

γ = [2

-1

, 2

-2

, ..., 2

-10

• The subset of N features producing the best results

in terms of accuracy is selected, using the classical

definition for sensitivity (STV), specificity (SPC)

and accuracy (ACC)

 =







 + 

 =







 + 

 =

( + )

 +  +  + 



(4)

with TP: true positives, TN: true negatives, FP:

false positives and FN: false negatives.

The performance indices STV, SPC and ACC for

each winner subset of N features corresponding to

FSA or FSB are estimated on the average of 1000

different runs over the 10 groups (1000 runs of the

10-fold cross validation). The results statistical

relevance was estimated by Wilcoxon rank sum test

(Mann-Whitney U) from feature averages and

standard deviations, and amplitude distribution bins,

independently for the male and female datasets.

4 RESULTS AND DISCUSSION

Table 1 gives comparative results after cross-

validation (1000 runs, 10-fold) in terms of STV, SPC

Parkinson’s Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions

365

and ACC and detection error (ERR) for male and

female datasets under a maximum p-value<10

-4

Rows 1 and 4 compare the standard features average

and standard deviation estimations from PD patients

vs healthy controls (1: males, 4: females). It may be

seen that the detection error is smaller for the female

dataset (4.144%) than for the male dataset (6.129%).

If the comparison is carried out using the amplitude

distributions (rows 2 and 5) the error is smaller for the

male dataset (5.235%) than for the female dataset

(7.783%). If both types of features are fused (rows 3

and 6) the errors are much smaller than when features

are separated and comparable (males: 2.127%,

females: 2.681%). If the features from the normative

datasets are fused on the reference set (rows 7 and 8)

the performance of the detection process is very much

enhanced regarding detection errors (males: 0.312%,

females: 0.408%).

Table 1: Merit figures for the different tests in % (M): Male

Sets; (F): Female Sets.

# Test Set Ref Set STV SPC ACC ERR

1(M)

avSFMPD

sdSFMPD

avSFMHC

sdSFMHC

93.871 93.871 93.871 6.129

2(M) avADMPD avADMHC 93.441 96.087 94.764 5.235

3(M)

avSFMPD

sdSFMPD

avADMPD

avSFMHC

sdSFMHC

avADMHC

95.800 99.946 97.873 2.127

4(F)

avSFFPD

sdSFFPD

avSFFHC

sdSFFHC

97.808 93.904 95.856 4.144

5(F) avADFPD avADFHC 97.821 86.612 92.217 7.783

6(F)

avSFFPD

sdSFFPD

avADFPD

avSFFHC

sdSFFHC

avADFHC

97.412 97.225 97.319 2.681

7(M)

avSFMPD

sdSFMPD

avADMPD

avSFMHC

sdSFMHC

avSFMNS

sdSFMNS

avADMHC

avADMNS

99.950 99.556 99.687 0.312

8(F)

avSFFPD

sdSFFPD

avADFPD

avSFFHC

sdSFFHC

avSFFNS

sdSFFNS

avADFHC

avADFNS

99.625 99.575 99.591 0.408

Nevertheless, this last situation is not the natural one,

as the normative dataset has been recruited under very

specific and controlled conditions (healthy young

subjects and well sustained phonation). Anyhow, the

results indicate that the detection capability of

amplitude distributions taken isolated are comparable

to those of standard feature parameters (averages and

standard deviations), and that their fused combination

produces substantial improvements when comparing

phonation features from PD patients with those from

healthy controls. This situation is closer to the real

conditions in which phonation tests are conducted, as

in daily life patient monitoring under limited quality

standards. If the reference set includes normative

subjects recorded under high quality standards the

detection errors decay strongly, as it could be

expected. The most relevant features regarding

detection in each experiment are given in Table 2.

Table 2: Ranked features by ReliefF in terms of joint

association with the binary outcome. The features in bold

are repeating in two or more experiments. The features in

italics are relative to distortion, unbalance or tremor. The

underlined features correspond to the first and fourth

quartile of the amplitude distributions. (M): Male Sets; (F):

Female Sets.

Feature Averages/Std.

Devs.

Amplitude Distributions

1(M)

18 45 47 49 57 62 66

18 33 42 49 59 61 63 69

70 72

2(M)

2 3 10 13 14 15 26 28 29 30

38 39 40 84 85 86 87 90 93

97 99

3(M)

20 21 24 39 42 52 62

7 61 69 70 95 98 100

4 27 41 58

4(F)

1 10 12 14 39 41 46 56

59 69

4 15 18 19 23 25 69

5(F)

2 4 5 7 8 57 58 59 61 62 66

6(F)

33 39 42 43 50 58 59

17 46 48 68 71 79

51 57 58 59 63 64

7(M)

9 11 13 14 32 69 72

3 4 6 14 20 38 90 92

5 9 11 20 33 47 52 56 59

8(M)

1 2 3 6 11 12 15 30 33

36 39 56 59 62 65

34 74 81 93 97 98 99

5 9 23 25 39 54 61 63 64

The table is organized as follows: each test shown in

Table 1 is indexed on the leftmost column. The

second column from the left gives the most relevant

average features in each upper cell, and the most

relevant standard deviation features in each lower

cell. The rightmost column gives the channel bin

indices from the Glottal Flow Amplitude

Distributions. It must be recalled that rows 1, 2, 3 and

7 refer to male subsets, where 4, 5, 6, and 8 refer to

female subsets. The features which appear more than

once in any of the experiments have been spotted in

bold. The features related with biomechanical

unbalance, perturbation or tremor have been spotted

italicized. In the rightmost column The Glottal Flow

Amplitude Distribution bins repeating in any of the

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different experiments have been spotted in bold. The

bins corresponding to the first and last quartiles have

been underlined. The review of the relevance features

reveals interesting facts:

• The number of features which repeat themselves

in the different experiments, taking into account

the averages, standard deviations and amplitude

distributions is relatively low:

Averages: 11(2), 14(2), 33(2), 39(4), 42(2), 56(2),

59(3), 62(3), 69(2);

Std. Devs.: 4(2), 5(2), 9(2), 18(2), 23(2), 25(2),

33(2), 59(4), 61(2), 63(3), 64(2), 69(2);

Ampl. Dist: 2, 4, 7 (1Q), 61 (3Q), 90, 93, 95, 97,

98, 99 (4Q).

The feature average repeating most (39, four times

out of six tests) is the vocal fold body losses

unbalance. The second most frequent are 59 (three

times) and 63 (three times), corresponding to the

relative Flow Gap and the relative Contact Gap.

Regarding the feature standard deviations

repeating most are 59 (four times out of six tests)

and 63 (three times), corresponding again to the

relative Flow Gap and the First Cyclical

Coefficient of vocal fold tremor (see c1 given by

expression (7) in Gómez et al., 2013b). Although

the most frequent features are related with

instability and closure defects, they do not appear

in all the tests, therefore, there is not a clear

repetition pattern of features with differentiation

power among the experiments.

• The amplitude distribution bins features which

show some repetition are distributed mainly in

the first quartile (Q1: 2, 4, 7) and mainly the

fourth quartile (Q4: 90, 93, 95, 97, 98, 99); only

a single bin is observed in the middle part of the

distribution (61: Q3); this observation indicates

that the most relevant parts of the distribution

differentiating altered from normative phonation

are the extreme quartiles, as it could be expected

from Figure 3 to Figure 7. Interestingly, feature

69, which corresponds to the amplitude of the

neuromotor tremor in the 4-8 Hz band (see

Mertens et al., 2013) appears twice as feature

average (in 4, test comparing PD vs HC samples

including female average and standard deviation

features, and 7, test comparing PD vs HC and NS

sets including female average, standard deviation

features and amplitude distributions) and as

feature standard deviations (in 1, test comparing

PD vs HC samples including male average and

standard deviation features, and 4, test

comparing PD vs HC samples including female

average and standard deviation features).

The consequence is that feature implication in

classification is not really uniform and depends

strongly on the experiment. This fact raises important

concerns regarding the way that classification

experiments are conducted the way it is generally

established: sets of features fused in supervectors

from databases limited by sample size, not

considering the semantic value of each feature. On the

one hand, it seems that high classification scores can

be achieved using well-known efficient classifiers.

On the other hand, it seems that there is not a clear

semantics on how classification depends on features.

The question is to which extent the overpower of

classification methods and the limited size of

databases are over-expressing the results allowing

good classification accuracy on very specific data, but

where semantic generalization is not well handled.

The burning question would be if seeking for new

features could be done maintain good phenomena-

linked semantics providing high classification scores

at the same time. Feature semantics should be the

main objective to be sought, as it allows the

formulation of new hypotheses based on functional

background. Promoting novel features with a remote

or no clear link with neurophysiological facts will not

allow going any further, no matter how good

classification scores may be. This is a relevant

question to be considered in proposing new features

and classification methods.

5 CONCLUSIONS

The present paper has been conceived to investigate

to two different questions. On the one hand, it is

intended to give an acoustical and physiological

explanation to defects in phonation which may be

induced by organic and neuromotor origin. On the

other hand, it is intended to show that good

classification scores between PD, HC and NS

phonation may be obtained basically fusing

distortion, biomechanical unbalance and tremor

feature averages and their standard deviations with

amplitude distributions obtained from the glottal flow

signal using a standard supervector classifier as an

SVM. The conclusions point out that these features

have differentiation capability, either taken isolated

or fused when differentiating PD vs HC and also

when differentiating PD vs HC and NS. In this last

case the best scores were obtained, with classification

errors under 0.5% (tests 7 and 8). Nevertheless, when

Parkinson’s Disease Glottal Flow Characterization: Phonation Features vs Amplitude Distributions

367

analyzing the semantics of the most frequent features

appearing as responsible of the classification scores,

there is not any feature common to the 8 tests carried

on. This means that classification scores depend more

on the collective power of several features, than on a

feature in particular. As semantics is in the nature of

the features themselves, this consequence casts some

suspicion that classification success is more on the

algorithmic machinery supporting it than on the clear

semantics which can be drawn from the experiments,

producing a certain “miraging” effect which needs to

be examined more in depth. Future lines or work are

being established to explore this paradoxical

controversy.

ACKNOWLEDGEMENTS

This work is being funded by grants TEC2016-

77791-C4-4-R from the Ministry of Economic

Affairs and Competitiveness of Spain,

CENIE_TECA-PARK_55_02 INTERREG V-A

Spain – Portugal (POCTEP), and 16-30805A,

LOl401, and SIX Research Center supported by the

Czech Republic Government. Special thanks are due

to Dra. I. Rektorova from St. Anne’s University

Hospital in Brno for her inspiring comments.

REFERENCES

E. R. Dorsey, et al. (2007). Projected number of people with

Parkinson disease in the most populous nations, 2005

through 2030, Neurology 68(5) 384-386.

W. Dauer and S. Przedborski (2003). Parkinson's disease:

Mechanisms and models Neuron, 39(6) 889–909.

J. Jankovic (2008). Parkinson's disease: clinical features

and diagnosis, J. Neurol. Neurosurg. Psychiatry 79(4),

368–376.

J. S. A. M. Reijnders, et al. (2008). Movement Disorders

23(2) 183-189.

J. Parkinson (1817). An Essay on the Shaking Palsy.

Originally published as a monograph by Sherwood,

Neely and Jones, London. Reproduced in J.

Neuropsychiatry Clin. Neurosci. 14(2), Spring 2002,

223-236.

L. Ricciardi, et al. (2016). Speech and gait in Parkinson’s

disease: When rhythm matters, Park. Relat. Disord. 32

42–47.

L. Brabenec, J. Mekyska, Z. Galaz, I. Rektorova (2017).

Speech disorders in Parkinson's disease: early

diagnostics and effects of medication and brain

stimulation, J. Neural Transm. 124(3) 303–334.

A. Tsanas (2012). Accurate telemonitoring of Parkinson’s

disease symptom severity using nonlinear speech signal

processing and statistical machine learning, PhD.

Thesis, U. of Oxford, U.K.

A. Gómez, D. Palacios, J. M. Ferrández, J. Mekyska, A.

Álvarez, P. Gómez (2019). Evaluating Instability on

Phonation in Parkinson’s Disease and Aging Speech,

Lecture Notes on Computer Science, 11487(2) 340-351.

I. Titze (1994), Principles of Voice Production, Prentice-

Hall, Englewood Cliffs, NJ.

M. Rothenberg, (1973). A new inverse-filtering technique

for deriving the glottal air flow waveform during

voicing, J. Acoust. Soc. Am. 53(6) 1632-1645.

G. Fant, and J. Liljencrants (1985). A four parameter model

of the glottal flow, STL-QPSR, 26(4) 1-13.

S. Mallat (1998). A wavelet tour of signal processing,

Academic Press, San Diego CA.

P. Gómez et al. (2013a). Wavelet description of the Glottal

Gap. 2013 18th International Conference on Digital

Signal Processing (DSP), Fira, Greece, 1-6.

doi: 10.1109/ICDSP.2013.6622718

J. Kreiman (2012). Variability in the relationship among

voice quality, harmonic amplitudes, open quotient, and

glottal area waveform shape in sustained phonation, J.

Acoust. Soc. Am. 132(4) 2625-2632.

P. Gómez et al., (2017). Parkinson's disease monitoring by

biomechanical instability of phonation,

Neurocomputing 255 3-16.

J. Mekyska, et al. (2015). Robust and complex approach of

patohogical speech signal analysis, Neurocomputing

167 94-111.

J. R. Deller, J. G. Proakis and J. H. L. Hansen (1993).

Discrete-Time Processing of Speech Signals,

Macmillan, New York.

P. Gómez et al. (2009). Glottal Source biometrical signature

for voice pathology detection, Speech Communication

51(9) 759-781.

A. R. Webb (2002). Statistical pattern recognition, John

Wiley & Sons, Chichester, UK.

I. Kononenko, E. Šimec, M. Robnik-Šikonja (1997).

Overcoming the Myopia of Inductive Learning

Algorithms with ReliefF. Applied Intelligence 7 39-55.

M. Robnik-Šikonja, I. Kononenko (2003). Theoretical and

empirical analysis of ReliefF and RReliefF. Machine

Learning 53(1–2) 23–69.

A. Gómez, et al. (2019). Evaluating Instability on

Phonation in Parkinson’s Disease and Aging Speech.

IWINAC 2019, LNCS, 11487 340-351.

C. Cortes and V. Vapnik (1995). Support-Vector Networks,

Machine Learning 20 273-297.

C. Chang and C. J. Lin (2011). LIBSVM: a library for

support vector machines, ACM Transactions on

Intelligent Systems and Technology, 2(3) 27:1-27:27.

P. Gómez et al. (2013b). Estimating Tremor in Vocal Fold

Biomechanics for Neurological Disease

Characterization. 2013 18th International Conference

on Digital Signal Processing (DSP), Fira, Greece, 1-6.

doi: 10.1109/ICDSP.2013.6622735.

C. Mertens, J. Schoentgen, F. Grenez, S. Skodda (2013),

“Acoustic Analysis of Vocal Tremor in Parkinson

Speakers”, Proc. of MAVEBA13 (Manfredi, C., Ed.),

Florence University Press, 19-22.

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