Clustering of Voice Pathologies based on Sustained Voice Parameters
Alessa Anjos de Oliveira
1,2
, Maria E. Dajer
2
, Paula O. Fernandes
1,4 a
and João Paulo Teixeira
1,3,4 b
1
Polytechnic Institute of Bragança, Campus Sta. Apolónia, 5301 857 Bragança, Portugal
2
Federal University of Technology of Paraná, Campus Cornélio Procópio, 86300 000, Cornélio Procópio, Brazil
³Research Centre in Digitalization and Intelligent Robotics (CEDRI), Bragança 5300, Portugal
4
Applied Management Research Unit (UNIAG), Bragança 5300, Portugal
Keywords: Voice Pathologies Clustering, Clustering with Boxplot, Voice Pathologies Analysis, Jitter Shimmer HNR and
Autocorrelation Statistical Analysis.
Abstract: Signal processing techniques can be used to extract information that contribute to the detection of laryngeal
disorders. The goal of this paper is to perform a statistical analysis through the boxplot tool from 832 voice
signals of individuals with different laryngeal pathologies from the Saarbrücken Voice Database in order to
create relevant groups, making feasible an automatic identification of these dysfunctions. Jitter, Shimmer,
HNR, NHR and Autocorrelation features were compared between several groups of voice
pathologies/conditions, resulting in three identified clusters.
1 INTRODUCTION
Healthy individuals are able to produce vibrations in
the vocal folds periodically and with almost constant
intensity (Cordeiro, 2016). "When there are
pathological changes to the larynx, the level of signal
and its fundamental frequency change" (Panek et al,
2015), thus, this sound variation caused by a disorder
in the vocal tract may be audible or visible in certain
characteristics obtained by signal processing.
The assessment to detect vocal pathologies is
considered invasive and relatively expensive
(Cordeiro et al, 2015). In addition, the investigation
made by a specialist physician, as shown in Zwetsch
et al (2006), is not always accurate, because certain
laryngeal changes may be similar in certain prisms,
although they are intrinsically different from each
other.
An alternative method for the recognition of
laryngeal disorders is automatic detection using
speech processing, which, in reverse to the traditional
artifice, "enables non-invasive, low cost and objective
assessment of the presence disorders" (Panek et al,
2015).
Signal processing can be used to extract a set of
parameters that contribute to the detection of
a
https://orcid.org/0000-0001-8714-4901
b
https://orcid.org/0000-0002-6679-5702
laryngeal disorders. For this, several authors (Guedes
et al, 2018; Teixeira J. P. et al, 2017; Teixeira F. et al,
2018; Fernandes J. et al, 2019) used recorded signal
of sustained vowels from individuals with a healthy
voice, as well as, patients who have some voice
disorder. Thus, it is possible to extract relevant
information that serves to identify the pathologic
subjects or even to classify the pathology. For this
purpose, the following parameters have been used
extensively: absolute jitter, relative jitter, absolute
shimmer, relative shimmer, Harmonic-to-Noise Ratio
(HNR), Noise-to-Harmonic Ratio (NHR) and
autocorrelation.
Most studies using artificial intelligence to
identify the speech samples into one of the
pathologies report the scarcity number of subject
available for each class in the existent databases
(Teixeira F. et al, 2018). Anyhow, some databases
have a large number of pathologies available with a
low number of subjects. If this slightly small number
of individuals of any pathology could be grouped
with subjects of a statistically similar pathology, it
could become a larger group with a significant
number of subjects.
The present work aims to perform a statistical
analysis through the boxplot tool, based on the paper
written by Teixeira J. P. et al (2018, p. 172). It is used
280
Anjos de Oliveira, A., Dajer, M., Fernandes, P. and Teixeira, J.
Clustering of Voice Pathologies based on Sustained Voice Parameters.
DOI: 10.5220/0009146202800287
In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS, pages 280-287
ISBN: 978-989-758-398-8; ISSN: 2184-4305
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
the previously quoted parameters taken from the
speech signal of 832 individuals for the possible
grouping of voice pathologies in order to create
relevant groups, making feasible an automatic
identification of these dysfunctions. The most
common symptoms that may indicate changes in the
larynx relate hoarseness, breathiness and roughness
(Teixeira J. P. et al, 2013, p 1112).
The section 2 of this document presents the
background of the boxplot statistical analysis,
followed with the description of material used in the
analysis in section 3. Section 4 describes the seven
parameters and for each one the statistical analysis
between the all pathologies. Finally, Section 5
presents the discussion and final conclusions together
2 DESCRIPTIVE STATISTICAL
ANALYSIS
The graphical presentation of a dataset made with the
boxplot or box-and-whisker plot uses five indicators:
the median, the first quartile, the third quartile, and
the smallest and largest number of the set (Mann,
2010). It helps to visualize the distribution and
skewness of the elements on the data set, in addition
to identifying outliers. To draft the box-and-whisker
plot is necessary to calculate the median, first
quartile, third quartile and interquartile range of these
elements (Hubert and Vandervieren, 2007). Then,
find the points that are 1.5 plus and minus this interval
in relation to the third and first quartile, respectively,
and, finally, the highest and lowest values of the set
are determined.
Figure 1: (a) First situation; (b) Second situation; (c) Third
situation.
According to Teixeira J. P. et al. (2018, p. 172) the
boxplot is used for a descriptive statistical analysis
and it compares the elements of a data set through
three situations: in the first situation there is no
overlap between the boxes, being B greater than A
(Figure 1(a)). Hence, there's a difference between
groups A and B. In the second situation (Figure 1(b))
the boxes overlay without their medians overlapping
the boxes, so there is, probably, a difference between
groups A and B. In the third situation (Figure 1(c))
the boxes overlap and their medians (at least one)
overlie the boxes. No difference can be considered
between groups A and B.
3 MATERIALS
The public available cured database described by
Fernandes et al (2019) was used in this work. This
database contains, among others, the seven
parameters used here, extracted by the algorithms
developed by Teixeira and Gonçalves (2016) and by
Fernandes et al (2018). This cured database was made
with the available features extracted from the
Saarbrücken Voice Database (SVD) (Barry and
Pützer). The SVD has, for each voice/subject, one
segment of voice record with the sustained vowels /a/,
/i/ and /u/ for High, Low and Neutral tones in a total
of nine speech segments. Each segment of the voice
consists in a steady state sustainable pronunciation of
the respective vowel. The individuals were analysed
and diagnosed by a physician. Many subjects
accumulated several voice pathologies, but for this
study, subjects with only one pathology were
considered. This was because the individuals with
several pathologies may interfere in the process of
segmentation of characteristics to identify the type of
dysfunction to be considered. Table 1 presents the
pathologies and its number of subjects available in the
cured database (Fernandes et al, 2019).
Table 1: Number of subjects for each pathology/condition.
Groups
Number of
Sub
j
ects
Carcinoma
Chronic Laryngitis
Control
Cyst
Functional Dysphonia
Granuloma
Hyperfunctional Dysphonia
Hypofunctional Dysphonia
Hypopharyngeal Tumor
Hypotonic Dysphonia
Intubation Granuloma
Laryngeal Tumor
Psychogenic Dysphonia
Reinke’s Edema
Spasmodic Dysphonia
Vocal Cord Paralysis
Vocal Cord Polyps
19
41
194
3
75
2
127
12
5
2
3
4
51
34
62
169
27
Clustering of Voice Pathologies based on Sustained Voice Parameters
281
For proper generalization, the sample size of each
group shall contain a relatively large number of
elements. Therefore, at least the pathologies Cyst,
Fibroma, Granuloma, Hypotonic Dysphonia,
Intubation Granuloma, Laryngeal Dysplasia are not
supposedly suited to any classification modelling
because they have a smaller sample size than the
‘relative large number of elements’. However, the
purpose of this work is to use pathologies with few
individuals in order to set them into relevant groups.
Thus, only fibroma and laryngeal dysplasia
dysfunctions will not be investigated because they
have only one individual.
4 PARAMETERS
CHARACTERIZATION AND
ANALYSIS
For this study, seven of the acoustic parameters
available in the cured database were used. The
parameters we considered were: absolute jitter,
relative jitter, absolute shimmer, relative shimmer,
HNR, NHR and autocorrelation.
4.1 Absolute Jitter
Absolute jitter, as pointed out by Teixeira J. P. et al.
(2018, p. 169), “is the glottal period variation between
cycles, that is, the mean absolute difference between
consecutive periods”. This description can be
presented in the form of an equation, as can be seen
in Equation 1.
𝑗
𝑖𝑡𝑡𝑎 =
1
𝑁−1
|
𝑇
−𝑇

|


(1)
The variable 𝑇
is the size of the glottal period and N
is the total number of glottal periods.
The comparison for absolute jitter was made
separating the genders as, in general, male voices
have low fundamental frequency and, consequently,
longer glottal periods. Therefore, for longer glottal
periods, there are larger deviations (Teixeira J. P. et
al 2018). Teixeira J. P. et al (2017) also highlights that
individuals, regardless of gender, have higher jitter
values when they cannot control the vibration of the
vocal folds.
4.1.1 Female Gender
In order to make an easier comparison Figure 2 shows
the boxplot of absolute jitter for all the pathologies
that are being worked on. In this framework, for cases
of hypopharyngeal tumor, hypotonic dysphonia and
granuloma caused by intubation, nothing can be
stated in the analyses, because there were not enough
female subjects in the database to print the boxplot.
Regarding all pathologies, a table can be created
with the samples and classify them according to the
boxplot presented in Figure 2, based on the previously
known theory shown in Figure 1. The number 1
points out the first situation (significant difference
between groups) as more appropriate, the number 2
presents the second situation (there is, probably, a
difference between groups) and the number 3
indicates the third situation (no difference between
the groups). Table 2 presents how were the analysis
made between each pathology with absolute jitter for
female gender in a succinct way.
4.1.2 Male Gender
In male-focused absolute jitter study, Table 3 shows
all pathologies together, pointing out the level of
intersection between them. Like in female gender
group, this table results from the statistical analyses
of all dysfunctions compared with themselves, like in
Figure 2.
4.2 Relative Jitter
According to Teixeira J. P. and Fernandes P. (2014),
this parameter “is the average absolute difference
between consecutive glottal periods divided by the
average period and expressed as a percentage”, as
shown in Equation 2.
𝑗
𝑖𝑡𝑡𝑒𝑟 =
1
𝑁−1
∑|
𝑇
−𝑇

|

1
𝑁
𝑇

×100 (2)
The descriptive statistical analysis presented in
Table 4 succinctly presents a comparison between the
studied pathologies and their possible classification
based on how the dysfunctions are connected each
other, according to the boxplot overlap of each pair of
pathologies/conditions.
4.3 Absolute Shimmer
Absolute shimmer (ShdB) refers to the amplitude
variation peak-to-peak of a sound wave, in decibels,
extracted from a recorded signal that generates an
extensive and sustained vowel. This parameter can be
enunciated as the absolute mean of the multiplication
between a constant of value 20 and a base 10
logarithm of the ratio between two consecutive
periods, as can be seen in Equation 3.
BIOSIGNALS 2020 - 13th International Conference on Bio-inspired Systems and Signal Processing
282
𝑆ℎ𝑑𝐵 =
1
𝑁−1
20×log
𝐴

𝐴



(3
)
The variable 𝐴
is the magnitude of the glottal
period and N is the total number of glottal periods.
Based on the information provided in Table 5 it is
possible to compare the pathologies and classify them
according to this parameter.
4.4 Relative Shimmer
One of the parameters for voice acoustical analysis
that affects the vocal quality of patients is the relative
shimmer, which is determined, according to Teixeira
J. P. et al (2018, p. 170) "as the mean absolute
difference between magnitudes of consecutive
periods, divided by the mean magnitude, expressed as
a percentage", as presented in Eq. 4.
𝑆ℎ𝑖𝑚 =
1
𝑁−1
∑|
𝐴

𝐴
|


1
𝑁
𝐴

×100 (4)
Table 6 shows the descriptive statistical analysis
of the relative shimmer parameter, which is used to
compare the pathologies each other based on the level
of intersection presented by their boxplot.
4.5 HNR
The HNR parameter represents the relationship
between the periodic and aperiodic components of a
speech segment, being the first component result
from the vibration of the vocal cords, while the
second component is a glottal noise. Several authors
inquire for different ways to express the HNR. In this
work, will be followed the Equation 5 presented by
Fernandes et al. (2018). According to the authors
HNR "consists in measure the energy of the first peak
of the normalized autocorrelation and consider that
this is the energy of the harmonic component of the
signal, and consider the remaining energy as the noise
energy given by the difference between 1 and the
harmonic energy”.
Table 7 resumes the comparison between
pathology groups and points out each one can be
considered the same group or not, based on the
existence of overlap between the boxplot’s groups,
like in Figure 2, and inform what is the level of
intersection between the groups.
𝐻𝑁𝑅(𝑑𝐵) = 10×log

𝑟
(𝜏

)
1−𝑟
(𝜏

)
(5)
The expression 𝑟
(𝜏

) is the maximum local
of the normalized autocorrelation.
4.6 NHR
Oppositely to the HNR, according to Fernandes
(2018), the NHR is the liaison between the aperiodic
component, more specifically the noise, and the
periodic component, related to the vibration of the
vocal cords. As this parameter is not measured in the
logarithmic domain, its values follow opposite
directions and are not exactly inverse to the HNR.
The Equation 6, which describes the NHR, is
determined as a function of the autocorrelation.
𝑁𝐻𝑅 = 1𝑎𝑢𝑡𝑜𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛
(6)
The investigation of pathologies with results
presented in Table 8, were conceivable to analyze and
classify the dysfunctions among themselves, in order
to shortly express the relation between pathologies,
according to this parameter.
Figure 2: Comparison of boxplot of the pathologies groups using absolute jitter for female voice signals.
Clustering of Voice Pathologies based on Sustained Voice Parameters
283
Table 2: Result of the analysis of boxplot for absolute jitter for female voice signals.
Table 3: Result of the analysis of boxplot for absolute jitter for male voice signals.
Table 4: Result of the analysis of boxplot for relative jitter.
4.7 Autocorrelation
The correlation of two signals, by itself, is the sum of
the values of their products. Therefore, as Fernandes
(2018) said, the autocorrelation can be elucidated as
the correlation of a signal with itself. The
autocorrelation function, for a sound wave, is a
method of detecting the periodicity of the signal, in
which the autocorrelation of the signal window is
divided by the autocorrelation of the window used, as
can be seen in Equation 7.
𝑟
(
𝜏
)
=
𝑟
(𝜏)
𝑟
(𝜏)
(7)
where the expression 𝑟
(𝜏) is the normalized
autocorrelation of part of the selected signal and
𝑟
(𝜏) is the normalized autocorrelation of the used
window.
Table 9 presents the level of connection between
the pathologies.
Vocal Par Spasm D Reinke's Psych D Laryn T Intub Gran Hypoph T Hypot D Hypof D Hyperf D Granul Func D Cyst Chron La
r
Carcin Control
Vocal Pol
33333 32232322
Vocal Par
3333 33232322
Spasm D
333 3 33333 12
Reinke's
33 1 21223 21
Psych D
333233313
Laryn T
11131321
Intub Gran
Hypoph T
Hypot D
Hypof D
33333 13
Hyperf D
3333 13
Granul
33 2 1 3
Funct D
33 1 3
33 1 3
Cyst
313
Chron La
r
12
Carcin
1
Vocal ParSpasm D Reinke's Psych D Laryn T Intub Gran Hypoph T Hypot D Hypof D Hyperf D Granul Func D Cyst Chron Lar Carcin Control
Vocal Pol
33331 3 3333133333
Vocal Par
333 2 3 3 33 31323 32
Spasm D
33 3 3 3 33 31323 32
Reinke's
31 3 3 3332333 23
Psych D
133333233333
Laryn T
11113111131
Intub Gran
33313333 33
Hypoph T
33 33333 23
Hypot D
333333 23
Hypof D
3333 3 23
Hyperf D
233 3 2 3
Granul
32 2 1 3
Funct
D
33 23
33 33
Cyst
323
Chron Lar
23
Carcin
2
Vocal Par Spasm D Reinke's Psych D Laryn T Intub Gran Hypoph T Hypot D Hypof D Hyperf D Granul Func D Cyst Chron Lar Carcin Control
Vocal Pol
333323 3333232322
Vocal Par
3332 3 3 333232332
Spasm D
332 3 3 3332323 32
Reinke's
32 3 3 33 31323 32
Psych D
13 3333233323
Laryn T
11111111131
Intub Gran
3333333323
Hypoph T
33 32333 23
Hypot D
332333 13
Hypof D
32333 23
Hyperf D
233 3 2 3
Granul
23 2 1 3
Funct D
33 23
33 23
Cyst
313
Chron Lar
23
Carcin
1
BIOSIGNALS 2020 - 13th International Conference on Bio-inspired Systems and Signal Processing
284
Table 5: Result of the analysis of boxplot for absolute shimmer.
Table 6: Result of the analysis of boxplot for relative shimmer.
Table 7: Result of the analysis of boxplot for HNR.
Table 8: Result of the analysis of boxplot for NHR.
Vocal ParSpasm D Reinke's Psych D Laryn T Intub Gran Hypoph
Hypot D Hypof D Hyperf D Granul Func D Cyst Chron Lar Carcin Control
Vocal Pol
3 3331 3 3 333132332
Vocal Par
3332 3 3 333133333
Spasm D
332 3 3 333133333
Reinke's
32 3 3 323122332
Psych D
1 3 3 333233323
Laryn T
1 1 111111131
Intub Gran
3 333233333
Hypoph
333233323
Hypot D
33333323
Hypof D
32333 23
Hyperf D
233 3 23
Granul
22 2 1 2
Funct
D
33 23
33 33
Cyst
323
Chron Lar
33
Carcin
2
Vocal Par Spasm D Reinke's Psych D Laryn T Intub GranHypoph
Hypot D Hypof D Hyperf D Granul Func D Cyst Chron Lar Carcin Control
Vocal Pol
3 3 331 3 3 333132 3 32
Vocal Par
3 3 32 3 3 33 3133 3 33
Spasm D
332 3 3 333133 3 33
Reinke's
32 3 3 32 3122 3 32
Psych D
13 3333233323
Laryn T
11111111131
Intub Gran
3333233333
Hypoph
33 3233 3 23
Hypot D
33333 3 23
Hypof D
3233 3 23
Hyperf D
233 3 23
Granul
22 2 1 2
Funct
D
3323
3333
Cyst
323
Chron Lar
33
Carcin
2
V
ocal Par Spasm D Reinke'sPsych D Laryn T Intub Gran Hypoph T Hypot D Hypof D Hyperf D Granul Func D Cyst Chron Lar Carcin Control
Vocal Pol
33331 3 3333233333
V
ocal Par
3 332 3 3 33 3233 3 33
Spasm D
331 3 3 33 3233 3 23
Reinke's
21 2 3 33 2122 3 32
Psych D
13 3333333323
Laryn T
11111111121
Intub Gran
3333333313
Hypoph T
33 3333 3 23
Hypot D
33333323
Hypof D
3333 3 23
Hyperf D
333 3 23
Granul
32 2 1 3
Funct
D
3323
3323
Cyst
313
Chron Lar
3
Carcin
1
Vocal Par Spasm DReinke'sPsych D Laryn T Intub Gran Hypoph T Hypot D Hypof D Hyperf D Granul Func D Cyst Chron Lar Carcin Control
Vocal Pol
333313 3333233333
Vocal Par
3332 3 3 3332333 33
Spasm D
331 3 3 33 32333 33
Reinke's
31 3 3 33 31323 32
Psych D
13 3333333323
Laryn T
11111111121
Intub Gran
3333333323
Hypoph T
33 33333 23
Hypot D
333333 23
Hypof D
3333 3 23
Hyperf D
333 3 23
Granul
33 2 1 3
Funct D
33 23
33 23
Cyst
313
Chron Lar
33
Carcin
2
Clustering of Voice Pathologies based on Sustained Voice Parameters
285
Table 9: Result of the analysis of boxplot for autocorrelation.
Table 10: Results from the analysis considering the all parameters.
5 DISCUSSIONS AND
CONCLUSIONS
The descriptive statistical analysis of each criteria
presents the situation that best fits each comparison
of the sets. By observing each factor among all the
disturbances, it was possible to assemble Table 10,
which exposes whether or not the pathologies can be
grouped based on these parameters.
When comparing two elements, it can be observed
the values arranged for each of the seven parameters.
Thus, if a comparison has a value other than the third
situation (3) this analysis can already indicate that the
sets cannot be clustered. This is because, in order for
the sets to be joined, all the parameters must present
similarities for both pathologies. Hence, Table 10
presents the expressions "YES" and "NO" indicating
that the pathologies may or may not be gathered, as
well as the red color for the sets marked with the
second expression “NO”
As shown in Table 10, carcinoma, granuloma and
laryngeal tumor cannot be grouped with other
pathologies. Reinke’s edema, spasmodic dysphonia,
paralysis and polyps in the vocal cords can be
clustered together due to the similarity with each
other. Hyperfunctional, hypophunctional, hypotonic
dysphonia, hypopharyngeal tumor and intubation
granuloma can be bundled with functional dysphonia
cyst and chronic laryngitis.
The pathologies spasmodic dysphonia, paralysis
and polyps in vocal cords can agglomerate with
chronic laryngitis, granuloma by intubation,
hypopharyngeal tumor, functional, hypofunctional
and hypotonic dysphonias, as all factors can be
associated to the third situation.
It is visible that some pathologies like Chronic
Laryngitis, Hypotonic Dysphonia and
Hypopharyngeal Tumor can be grouped with all the
dysfunctions, except the groups that are not arranged
with any dysfunction.
Therefore, it is noticeable that the pathologies
with resemblance can be clustered, while divergent
dysfunctions are kept away from the other groups.
This work, in the future, can be used to confirm if
the use of Artificial Intelligence for clustering of
pathological diseases exhibit satisfactory results,
Vocal Par Spasm DReinke'
s
Psych D Laryn T Intub Gran Hypoph T Hypot D Hypof D Hyperf DGranul Func D Cyst Chron Lar Carcin Control
Vocal Pol
3 3 331 3 3 33 32333 33
Vocal Par
3332 3 3 3332333 33
Spasm D
331 3 3 33 32333 33
Reinke'
s
31 3 3 33 32233 32
Psych D
13 3333333323
Laryn T
11111111121
Intub Gran
3333333323
Hypoph T
33 33333 23
Hypot D
333333 23
Hypof D
3333 3 23
Hyperf D
333 3 2 3
Granul
33 2 1 3
Funct D
33 23
33 23
Cyst
313
Chron Lar
33
Carcin
2
V
ocal Pa
r
Spasm D Reinke's Psych D Laryn T Intub Gran Hypoph T Hypot D Hypof D Hyperf D
G
ranu
l
Func D Cyst Chron Lar Carcin Control
Vocal Pol
YES YES YES YES NO YES YES YES YES NO NO YES NO YES NO NO
V
ocal Pa
r
YES YES YES NO YES YES YES YES YES NO YES NO YES NO NO
Spasm D
YES YES NO YES YES YES YES YES NO YES NO YES NO NO
Reinke's
NO NO NO YES Y ES NO NO NO NO NO Y ES NO NO
Psych D
NO YES YES YES YES YES NO YES YES YES NO YES
Laryn T
NO NO NO NO NO NO NO NO NO NO NO
Intub Gran
YES YES YES NO NO YES YES YES NO YES
Hypoph T
YES YES YES NO YES YES YES NO YES
Hypot D
YES YES NO YES YES YES NO YES
Hypof D
YES NO YES YES YES NO YES
Hyperf D
NO YES YES YES NO YES
Granu
l
NO NO NO NO NO
Funct D
YES YES NO YES
YES YES NO YES
Cyst
YES NO YES
Chron Lar
NO NO
Carcin
NO
BIOSIGNALS 2020 - 13th International Conference on Bio-inspired Systems and Signal Processing
286
since the sets of these pathologies, in this study,
already shows what pathologies can be clustered.
ACKNOWLEDGEMENTS
This work was supported by FCT Fundação para a
Ciência e Tecnologia within the Projects:
UIDB/04752/2020 and UIDB/5757/2020.
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