Analysis and Classification of Voice Pathologies using Glottal Signal

Parameters with Recurrent Neural Networks and SVM

Leonardo Forero Mendoza

, Manoela Kohler

, Cristian Muñoz

, Evelyn Conceição Santos Batista

and Marco Aurélio Pacheco

Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil

Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil

Keywords: Classification of Vocal Folds Pathologies, Glottal Signal Parameters, Neural Network, Deep Learning.

Abstract: The classification of voice diseases has many applications in health, in diseases treatment, and in the design

of new medical equipment for helping doctors in diagnosing pathologies related to the voice. This work uses

the parameters of the glottal signal to help the identification of two types of voice disorders related to the

pathologies of the vocal folds: nodule and unilateral paralysis. The parameters of the glottal signal are obtained

through a known inverse filtering method and they are used as inputs to an Artificial Neural Network, RNN,

LSTM, a Support Vector Machine and also to a Hidden Markov Model, to obtain the classification, and to

compare the results, of the voice signals into three different groups: speakers with nodule in the vocal folds;

speakers with unilateral paralysis of the vocal folds; and speakers with normal voices, that is, without nodule

or unilateral paralysis present in the vocal folds. The database is composed of 248 voice recordings (signals

of vowels production) containing samples corresponding to the three groups mentioned. In this study a larger

database was used for the classification when compared with similar studies, and its classification rate is

superior to other studies, reaching 99.2%.

1 INTRODUCTION

The diagnosis of voice pathologies currently requires

invasive endoscopy procedures, such as laryn-

gostroboscopy or surgical microlaryngoscopy.

However, one wants to aid the pre-diagnosis of the

vocal folds pathologies with computer-based,

decision support diagnostic tools using voice signals.

Two pathologies related to the vocal folds will be

considered here: nodules and unilateral paralysis

(Roy N et al., 2017) (Francis D. O. At al., 2014).

Vocal cord nodules are growth on both vocal folds

caused by their repeated and incorrect usage, which

permits the developing of swollen spots on them. The

nodules will become larger and stiffer the longer the

vocal incorrect usage continues. Singers, teachers and

announcers are examples most probably to have this

kind of pathology in the vocal folds (Francis D. O. At

al., 2014). Unilateral vocal fold paralysis (UVFP)

occurs from a dysfunction of the recurrent laryngeal

or vagus nerve innervating the larynx. It causes a

characteristic breathy voice often accompanied by

swallowing disability, a weak cough, and the

sensation of shortness of breath. This is a common

cause of neurogenic hoarseness. When this paralysis

is properly evaluated and treated, normal speaking

voice is typically restored (Steffen N Pedrosa V. V.

and Kazuo R., Pontes P, 2009) (Behlau M, Pontes PP,

1995).

The aim here is to evaluate the use of glottal

signals (signal obtained just after the vocal folds and

before the vocal tract) for providing better

classification models of the pathologies discussed

above. The most common method for extracting

voice features is directly from the voice signal (Roy

N et al., 2017).

However, many researchers have looked for some

characteristics extracted from the glottal signal, not

only for identifying pathologies related to the vocal

folds, but also to other applications, as to synthesize

voice (Henrich, N., 2001)( Henrich N, d'Alessandro

C., 2014) or identifying vocal aging (Mendonza L.,

Vellasco M., Cataldo E., 2014).

The process of obtaining the glottal signal, from

the voice signal, has been facilitated due to the

development of algorithms which can perform an

Mendoza, L., Kohler, M., Muñoz, C., Batista, E. and Pacheco, M.

Analysis and Classiﬁcation of Voice Pathologies using Glottal Signal Parameters with Recurrent Neural Networks and SVM.

DOI: 10.5220/0007250700190028

In Proceedings of the 11th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2019), pages 19-28

ISBN: 978-989-758-350-6

inverse filtering from the voice signal, eliminating the

influence of the vocal tract (Software Aparat).

Different methods have been used to classify

diseases related to the voice, such as Hidden Markov

Models (HMM) (Francis D. O. At al., 2014),

Gaussian Mixture Models (GMM) and Artificial

Neural Networks (Steffen N Pedrosa V. V. and

Kazuo R., Pontes P, 2009), all of them using as inputs

Mel-Frequency Cepstral Coefficients (MFCC) and

parameters such as jitter and shimmer. However, as

most voice disorders are due to some disorder on the

vocal folds dynamics, it is best to work with

parameters extracted from the glottal signal, since the

signal is produced by the vocal folds.

In (Rosa I. S., 2005) (Londoño J., Llorente J.,

2010), (Wang X, Zhang J, Yan Y, 2009) the Mel-

frequency cepstral coefficients (MFCC) were used as

input parameters to classify pathologies. A database

composed of 12 recordings for men and women,

resulting in a maximum performance of 80%

accuracy (Londoño J., Llorente J., 2010). MFCC have

also been proved to be effective in speaker

recognition problems. However, their performance is

not as effective in the classification of voice

pathologies. In (Rosa I. S., 2005), several models for

the classification of voice pathologies are compared.

The best performance has been provided by a neural

network based model, differing from speaker

recognition applications where best results are

usually obtained with GMM and HMM. This is

probably because classification of voice pathologies

does not fully depend on temporal features of the

voice, and the pathology causes change in the voice

signal (Hariharan, M., 2009).

Therefore, the main objective of this work is to

evaluate the performance of voice pathologies

classification models based on parameters extracted

from the glottal signal. Additionally, a new database

was created, with a larger number of voice

recordings, which allows a better evaluation of the

influence of each parameter in the classification

performance.

This paper is organized as follows. Methods

section explains how the glottal signal is obtained and

how the features, extracted from the glottal signal, are

used. Proposed Methodology section presents the

three classifiers evaluated in this paper, so their

performance can be evaluated in voice pathologies

classification: Neural Network, Support Vector

Machine and Hidden Markov Model. Results section

presents the database details, results obtained and

their analysis. Lastly, Conclusions are outlined in the

final section.

2 METHODS

2.1 The Glottal Signal

The voice signal production, particularly the one

related to voiced sounds, e.g. vowels, starts with the

contraction-expansion of the lungs, generating a

pressure difference between the air in the lungs and

the air near the mouth. The airflow created passes

through the vocal folds, which oscillate in a frequency

called the fundamental frequency of the voice. This

oscillation modifies the airflow coming from the

lungs, changing it into air pulses. The pressure signal

formed by the air pulses is quasi-periodic and it is

called the glottal signal (M. D. O. Rosa., 2000).

2.2 Features Extracted from the

Glottal Signal

The glottal signal is obtained performing an inverse

filtering on the voice signal, which consists on

eliminating the influence of the vocal tract and the

voice radiation caused by the mouth, preserving the

glottal signal characteristics (Pulakka H., 2005). The

inverse filtering algorithm used here is the so-called

PSIAIF (Pitch Synchronous Iterative Adaptive

Inverse Filtering) (Mendonza L., Vellasco M.,

Cataldo E., 2014) (Pulakka H., 2005). It was chosen

due to its high performance and ease development.

There is a toolbox implementation in Matlab®, called

Aparat (Software Aparat), which was constructed

especially based on the PSIAIF method to obtain the

glottal signal and to extract its main features or

parameters. The parameters that will be used can be

divided into three groups: time domain, frequency

domain, and the ones that represent the variations of

the fundamental frequency. More details about these

parameters can be found in (Pulakka H., 2005).

2.2.1 Time-domain Parameters of the

Glottal Signal

The time domain parameters which can be

extracted from the glottal signal are described below

(Wang X, Zhang J, Yan Y, 2009) (Pulakka H., 2005).

 Closing phase (Ko): describes the interval

between the instant of the maximum opening of

the vocal folds and the instant where they close

(M. D. O. Rosa., 2000);

 Opening phase (Ka): describes the interval

between the instant where the vocal folds start

the oscillation up to their maximum opening

(M. D. O. Rosa., 2000);

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

 Open quotient (OQ): The ratio between the

total time of the vocal folds opening and the

total time of a cycle (or period) of the glottal

signal (T). It is inversely proportional to the

intensity of the voice. The smaller it is, the

higher the voice intensity (Wang X, Zhang J,

Yan Y, 2009) (Pulakka H., 2005);

 Close quotient (CIQ): The ratio between the

closing phase parameter (Ko) and the total

length of a glottal pulse (T) (Pulakka H., 2005).

It is inversely proportional to the voice

intensity. The smaller it is, the higher the voice

intensity;

 Amplitude quotient (AQ): The ratio between

the glottal signal amplitude (Av) and the

minimum value of the glottal signal derivative

[16]. It is re-lated to the speaker phonation

(Pulakka H., 2005);

 Normalized amplitude quotient (NAQ): It is

calculated by the ratio between the amplitude

quotient (AQ) and the total time length of the

glottal pulse (T) (Pulakka H., 2005);

 Open quotient defined by the Liljencrants-Fant

model (OQa): This is another opening quotient

but calculated by the Liljencrants-Fant model

for inverse filtering. Details about this model

can be found in (Wang X, Zhang J, Yan Y,

2009);

 Quasi open quotient (QoQ): It is the

relationship between the glottal signal opening

at the exact instant of the oscillation and the

closing time. It has been used in some works to

classify emotions (Wang X, Zhang J, Yan Y,

2009);

 Speed quotient (SQ): defined as the ratio of the

opening phase length to the closing phase

length (Pulakka H., 2005);

2.2.2 Frequency Domain Parameters

 Difference between harmonics (DH12): Also

known as H1-H2, it is the difference between

the values of the first and second harmonics of

the glottal signal (Wang X, Zhang J, Yan Y,

2009) (Pulakka H., 2005). This parameter has

been used to measure vocal quality;

 Harmonics richness factor (HRF): relates the

first harmonic (H1) with the sum of the energy

of the other harmonics (Hk) (Pulakka H.,

2005). It has also been used to measure vocal

quality;

2.2.3 Parameters that Represent Variations

and Perturbations in the Fundamental

Frequency

 Jitter: variations in fundamental frequency

between successive vibratory cycles (Wang X,

Zhang J, Yan Y, 2009) (Pulakka H., 2005).

Changes in jitter may be indicative of

neurological or psychological difficulties (Roy

N et al., 2017);

 Shimmer: variations in amplitude of the glottal

flow between successive vibratory cycles

(Wang X, Zhang J, Yan Y, 2009) (Pulakka H.,

2005). Changing the shimmer is found mainly

in the presence of mass lesions in the vocal

folds, such as polyps, edema, or carcinomas

(Roy N et al., 2017);

3 PROPOSED METHODOLOGY

FOR VOICE PATHOLOGIES

CLASSIFICATION

The proposed model used has two stages: the first

stage is the features extraction, where all the above

mentioned parameters from the glottal signal are

obtained; the second stage is the classification

module, where four algorithms have been selected to

classify different pathologies of the voice - a

multilayer perceptron (MLP) neural network, a

support vec-tor machine, Long short-term memory

(LSTM) and a Hidden Markov Model (HMM), for

comparison reasons. The proposed methodology is

illustrated in Figure 1 and each model is described in

the following sub-sections. A similar methodology

has been already applied for classifying voice aging

(Mendonza L., Vellasco M., Cataldo E., 2014), with

very good results.

Figure 1: Methodology used for the classification of voice

pathologies.

3.1 Inverse Filtering

For each vocal utterance a corresponding glottal

signal is obtained by inverse filtering (PSIAIF meth-

od) and the parameters are extracted using the Aparat

(Software Aparat) and Praat (Software Praat)

software. The following parameters are obtained:

Analysis and Classiﬁcation of Voice Pathologies using Glottal Signal Parameters with Recurrent Neural Networks and SVM

fundamental frequency (fo), jitter, shimmer, Ko, Ka,

NAQ, AQ, CIQ, OQ1, OQ2, Oqa, Qoq, SQ1, SQ2,

DH12, and HRF. The parameters are separated

according to the groups to which they belong. In

particular, OQ was divided into OQ1 and OQ2, the

open quotients calculated from the so-called primary

and secondary openings of the glottal flow. The

difference between OQ1 and OQ2 is that OQ1 is

calculated from the closure of the glottal flow until

the closure of the next glottal flow, and OQ2 is

calculated from de opening until the closure of the

glottal flow; SQ, as well, was divided into speed

quotients calculated from the primary and secondary

openings of glottal signal. It is important to mention

that some parameters provide similar information,

but, in this phase, all of them will be considered.

3.2 Classification Module

For the classification of voice pathologies four

classifiers have been used: Artificial Neural

Networks (ANN), Support Vector Machine (SVM),

LSTM and Hidden Markov Models (HMM).

For the ANN classifier, a multi-layer perceptron

(MLP) structure, trained with the back-propagation

algorithm, was chosen, since it is a universal

approximator. Different topologies were examined

with different numbers of neurons in the hidden layer

to seek the best generalization performance. For the

SVM classifier, different kernels (polynomial, radial

basis function (RBF), and sigmoid) and different

values for the normalization coefficient (C) were

evaluated to determine the optimal settings. Final-ly,

an Estimate-Maximize (Baum Welch) approach was

used to train three HMM models (one HMM for each

output class), each one to maximize the likelihood of

the training data with respect to the unknown

parameters. To classify a sequence into one of the

three classes, the log-likelihood given by each model

is computed, and the most likely model defines the

class that the test sequence belongs to. Left-to-right

HMM models with five states and three Gaussian

mixtures were trained in order to obtain an optimal

classification rate.

4 RESULTS

4.1 Database

Most of the works on vocal folds diseases

classification just classify speakers into two groups:

speakers with disease (all kinds of disease) and

speakers with normal voices (Rosa I. S., 2005),

(Londoño J., Llorente J., 2010). In this work the type

of disease is also identified, helping in the indication

if the patient has nodule or paralysis on the vocal

folds, or neither one.

The developed database is composed of 248

records consisting of voices of both genders, women

and men, with different ages, and it is divided into

three groups: 12 speakers with nodule on the vocal

folds; 8 speakers with vocal folds paralysis; and 11

speakers with normal voices. Eight voice records

were taken from each speaker. This database was

obtained from a speech therapist in Rio de Janeiro

among people in treatment.

For the recordings is used a computer, the Doctor

speech software and an omnidirectional microphone.

The voices were recorded in a doctor's office.

The speakers belonging to the pathology groups

(nodule and paralysis) have different categories of the

disease in each group, as described in Tables 1 and 2.

The following tables describe the speakers in more

details.

Table 1: Speakers with Nodule on the Vocal Folds (F –

Female, M – Male).

Speaker

Gender

Age

Description of

the disease

Speaker 1

Bilateral nod-

ules causing a

small irregular

vocal cord

chink

Speaker 2

Bilateral nod-

ules with mid-

posterior chink

Speaker 3

Vocal nodules

with moderate

and severe

anterior and

posterior

irregular chinks

Speaker 4

Vocal nodules

with an

irregular vocal

cord chink

Speaker 5

Vocal nodules

with an

irregular vocal

cord chink

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

Table 1: Speakers with Nodule on the Vocal Folds (F –

Female, M – Male) (Cont.).

Speaker 6

Vocal nodules

with mid-

posterior chink

Speaker 7

Vocal nodules

with mid-

posterior chink

Speaker 8

Fibrous nodules

- mid-posterior

chink - great

vocal effort

Speaker 9

Vocal nodules

with mid-

posterior chink

Speaker 10

Vocal

nodules with

an irregular

vocal cord

chink

Speaker 11

Vocal

nodules with

a slight

irregular

vocal cord

chink

Speaker 12

Vocal

nodules with

mid-posterior

chink

Table 2: Speakers with Vocal Folds Paralysis (F – Female,

M – Male).

Speaker

Gender

Age

Description

of the disease

Speaker 13

Right vocal

fold paralysis

with scar re-

traction in the

middle 1/3 -

anterior spin-

dle chink

(lar-yngeal

trauma

sequel)

Speaker 14

Right

hemilarynx

idiopathic

paralysiswith

slight vocal

cord bowing

Speaker 15

Right vocal

cord paralysis

with spindle

chink

Speaker 16

Right vocal

cord paralysis

paramedian

posi-tion with

a slight

bowing and a

slight

spindle chink

- paralytic

falsetto

Speaker 17

Left vocal

cord paralysis

in the left

median and

paramedian

positions –

no chinks

Speaker 18

Right

hemilarynx

idio-pathic

paralysis -

para-median

position

Speaker 19

Left vocal

cord paralysis

with a slight

bow-ing

(intubation

trauma)

Speaker 20

Right vocal

cord paralysis

paramedian

position - left

vocal fold

stiffness

Analysis and Classiﬁcation of Voice Pathologies using Glottal Signal Parameters with Recurrent Neural Networks and SVM

Table 3: Speakers with No Disease (F - Female, M – Male).

Speaker

Gender

Age

Speaker 21

Speaker 22

Speaker 23

Speaker 24

Speaker 25

Speaker 26

Speaker 27

Speaker 28

Speaker 29

Speaker 30

Speaker 31

4.2 Analysis of the Parameters for

Classification

In order to evaluate the influence of each input

parameter in the classification of voice diseases, the

box-plot [24] function was used. The boxplot was

constructed for each of the parameters extracted from

the glottal signal, in order to see their influence in

each type of pathology or normal voices and to

compare their behavior. The three boxplots for each

group are related to Nodule, Paralysis and Normal

Voices, respectively. To facilitate the analysis and

better understand the parameters variation, their

boxplots were grouped and analyzed by each type of

parameter: Time-domain parameters, Frequency-

domain parameters and Parameters that Represent

Variations and Perturbations in the Fundamental

Frequency, as described in the following sub-sections

and in Figures 2 to 15.

4.2.1 Time-domain Parameters of the

Glottal Signal

The following figures shows the corresponding

boxplots for the so-called time-domain parameters

extracted from the glottal signal, where some

interesting observations can be extract-ed. The

parameter Ko, which shows the closing phase of the

vocal folds, is higher in normal voices than in voices

with the pathologies considered (Figure 2). OQ1,

OQ2, CIQ, AQ and NAQ parameters (Figure 4 to 8)

imply that normal voices have more intensity and

better voice quality when compared with pathologies.

The values of the parameters SQ1 and SQ2 are lower

in normal voices, which indicate a shortening in the

structure of the vocal folds when one has these

diseases, especially paralysis (Figures 11 and 12).

Figure 2: Closing phase(Ko).

Figure 3: Opening phase(Ka).

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

Figure 4: Open quotient(OQ1).

Figure 5: Open quotient (OQ2).

Figure 6: Close quotient(CIQ).

Figure 7: Amplitude quotient (AQ).

Figure 8: Normalized amplitude quotient (NAQ).

Figure 9: Open quotient defined by the Liljencrants-Fant

model (OQa).

Figure 10: Quasi opening quotient (QoQ).

Figure 11: Speed quotient (SQ1).

Analysis and Classiﬁcation of Voice Pathologies using Glottal Signal Parameters with Recurrent Neural Networks and SVM

Figure 12: Speed quotient (SQ).

4.2.2 Frequency Domain Parameters

Figures 13 to 15 show the corresponding boxplots

related to the frequency domain parameters.

Figure 13: Fundamental Frequency (F0).

Figure 14: Difference between harmonics (DH12).

Figure 15: Harmonics richness factor (HRF).

The fundamental frequency (Fig. 13) has a wide

variation for voices with unilateral paralysis, showing

a greater disturbance in the vocal folds. Voices with

nodules have less variation of fundamental frequency

when compared with normal voices. Harmonics

richness factor (Fig. 15) changes a lot for unilateral

paralysis.

4.2.3 Parameters That Represent Variations

and Perturbations in the Fundamental

Frequency

Figures 16 and 17 present the boxplots of the

parameters directed related to the variations and

perturbations of the fundamental frequency.

Figure 16: Jitter.

Figure 17: Shimmer.

As can be seen from these boxplots, in the

pathologies cases, the function of the vocal folds is

greatly compromised, which is indicated by jitter and

shimmer parameters, as shown in Figures 16 and 17.

Jitter and shimmer parameters vary the most in the

voice when paralysis occurs. Jitter and Shimmer are

very high in voices with paralysis, proving to have

affected the most the vocal folds.

4.2.4 Analysis of the Classification Results

Classification of the pathologies was performed using

four different classifiers: ANN, SVM, LSTM and

HMM. For each classifier, three cases were

considered for the input parameters: (i) only the

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

parameters extracted from the glottal signal, (ii) only

the MFCCs, and (iii) a combination of (i) and (ii). The

results for each input configuration are presented in

the following sub-sections.

Classification Results with the Parameters of the

Glottal Signal. In this case, the inputs of the

classifiers are 16 parameters of the glottal signal. The

original database was divided into training, validation

and test sets, where 70% of the database was used for

training, 20% for validation, and 10% for testing. For

ANN (after performing lots of tests varying the

number of the neurons in the hidden layer) the best

result was obtained with 8 processors in the hidden

layer.

Considering the SVM as the classifier, the best

result was achieved when a RBF kernel was used with

a regularization constant of C=1 and a Gaussian

standard deviation of σ=1.

Our model is a deep recurrent neural network with

two layers of 100 LSTM cells each. The bottommost

layer is the input layer where we inject each time

frame of an individual example at each time step. The

layer contains 13 units that would contain the

coefficients of the time frames. The next layer is two

layers are LSTM recurrent layers.

An Estimate-Maximize (Baum Welch) approach

was used to train three HMM models (one HMM for

each class), each one to maximize the likelihood of

the training data with respect to the unknown

parameters. To classify a sequence into one of the

three classes, the log-likelihood given by each model

is computed, and the most likely model defines the

class that the test sequence belongs to. Left-to-right

HMM models with five states and three Gaussian

mixtures were trained in order to obtain an optimal

classification rate.

The classifiers has three outputs: speakers with

nodule on the vocal folds, containing 93 voice

records, speaker with vocal folds paralysis,

containing 67 records, and speaker with normal voice,

containing 88 records.

4.2.5 Classification Results with

Mel-Frequency Cepstral Coefficients

(MFCCS)

Mel-Frequency Cepstral Coefficients (MFCCs) are

coefficients that collectively make up an MFC and are

derived from a type of cepstral representation of the

audio clip (a nonlinear "spectrum-of-a-spectrum").

MFCCs are common in speaker recognition, which is

the task of recognizing people from their voices. 12

MFC coefficients were used, the number most often

used in the literature (Rosa I. S., 2005) (Londoño J.,

Llorente J., 2010).

The inputs of the classifiers are, therefore, 12

MFC coefficients in this case. The original database

was divided into training, validation and test sets,

where 70% of the database was used for training, 20%

for validation, and 10% for testing, as in the previous

case. After lots of tests varying the number of the

neurons, the best result was achieved with 6

processors in the hidden layer. Considering the SVM

as the classifier, the best result was achieved when a

RBF kernel was used with a regularization constant

of C= 0,8 and a Gaussian standard deviation of σ=1.

HMM configuration is the same as above.

LSTM had the best performance with 100 cells.

4.2.6 Classification Results with Combining

MFCCs and Glottal Signal Parameters

In this third configuration, the input vector of the

classifiers is composed of 12 MFC coefficients and

16 parameters of the glottal signal. The original

database was also divided into training, validation

and test sets, where 70% of the database was used for

training, 20% for validation, and 10% for testing.

After lots of tests, the best ANN configuration was

obtained with 9 processors in the hidden layer.

Considering the SVM as the classifier, the best result

was achieved when a RBF kernel was used with a

regularization constant of C= 2 and a Gaussian

standard deviation of σ=1. HMM and LSTM

configuration is the same as above.

4.2.7 Discussion

Table 4 presents a summary of the results obtained

with all three classifiers and all four configurations of

input signals. As can be seen from the results in Table

4, the classification was successful with the glottal

signal parameters, despite having an imbalanced

database (fewer samples for voices with paralysis)

and factors such as gender and age difference

between speakers, reaching the conclusion that these

parameters are good discriminators for classifying

voice disorders.

When using only MFCC parameters, the best

result is obtained with the LSTM classifier, since its

stochastic behaviour can better handle temporal

samples.

The combination of MFCCs and glottal signal

parameters provided the best classification results,

with an increase of 1% in the average performance

when compared with the results with only glottal

signal parameters. The best classification

performance was obtained with the LSTM classifier,

Analysis and Classiﬁcation of Voice Pathologies using Glottal Signal Parameters with Recurrent Neural Networks and SVM

with over 98,3% accuracy. The results were obtained

by Intel® Optimization for TensorFlow. The LSTM

network was run on a gold Xeon processor showing

faster speed than on a 1080 nvidea graphics board in

30% under the same conditions.

Table 4: Classification of Voice Pathologies.

Parameters

ANN

HMM

LSTM

SVM

Parameters of

the glottal

signal

95.8%

82%

97%

96.2%

MFCCs

75.2%

87%

94%

80%

Glottal signal

parameters

and MFCCs

96.6%

92%

98.3%

97.2%

LSTM

Xeon

1080 Nvidia

527 sec

742 sec

Training

time

5 CONCLUSIONS

The aim of this work was the classification of two

voice diseases: nodule and unilateral paralysis and the

evaluation of the impact of parameters from the

glottal signal on this identification. Three different

classifiers have been used, to compare their

performance: an Artificial Neural Network, a Support

Vector Machine, LSTM and a Hidden Markov

Model.

From the results obtained, it can be verified that

glottal signal parameters are more relevant to

discriminate pathologies of the vocal folds than

MFCC’s, when they are evaluated individually. This

is the case even when the database is composed of

individuals with different genders and ages, providing

an average accuracy over 99%.

ACKNOWLEDGMENTS

This work was supported by Intel Corporation.

REFERENCES

Roy, N., Holt, K. I., Redmond, S., Muntz, H., 2007,

Behavioral characteristics of children with vocal fold

nodules. J Voice. 21(2):157-68.

Francis, D. O.; McKiever, M. E.; Garrett, G., Jacobson, B.;

Penson, D. F., 2014, Assessment of Patient Experience

with Unilateral Vocal Fold Immobility: A Preliminary

Study, Journal of voice 28 (5), 636-643.

Steffen, N., Pedrosa, V. V., Kazuo, R., Pontes, P., 2009,

Modifications of Vestibular Fold Shape from

Respiration to Phonation in Unilateral Vocal

FoldParalysis, Journal of Voice, Vol. 25, No. 1, pp.

111-113.

Behlau, M., Pontes, P. P., 1995, Avaliação e tratamento das

disfonias. São Paulo: Lovise, in unilateral vocal fold

paralysis, Journal of Voice 13(1):36-42.

Henrich, N., 2001, Étude de la source glottique en voix

parlée et chantée modelisation et estimation, mesures

acoustiques et electroglottographiques, perception,

Thèse de doctorat de l'Université Paris 6 (PhD Thesis).

Henrich, N., d'Alessandro, C., Doval, B., Castellengo, M.

2005, Glottal Open quotient in singing: Measurements

and correlation with laryngeal mechanisms, vocal

intensity, and fundamental frequency, Journal of the

Acoustical Society of America 117(3), pp 1417-1430.

Mendonza, L., Vellasco, M., Cataldo, E., 2014,

Classification of Vocal Aging Using Parameters

Extracted From the Glottal Signal J Voice. 21(2):157-

68.

Software Aparat, http://aparat.sourceforge.net/index.php/

Main_Page, Helsinki University of Technology

Laboratory of Acoustics and Audio Signal Processing.).

Rosa, I. S., 2005, Análise acústica da voz de indivíduos na

terceira idade, Tese de mestrado Universidade de São

Paulo, São Carlos (in portuguese).

Londoño, J., Llorente, J., 2010, An improved method for

voice pathology detection by means of a HMM-based

feature space transformation Pattern Recognition,

Volume 43, Issue 9, September 2010.

Wang, X., Zhang, J., Yan, Y., 2009, Glottal Source

biometrical signature for voice pathology detection,

Speech Communication, 51 759-781.

Hariharan, M., Paulraj, M. P., Yaacob, S., 2009,

Identification of vocal fold pathology based on Mel

Frequency Band Energy Coefficients and singular

value decomposition Signal and Image Processing

Applications (ICSIPA), volume 514 – 517.

Rosa, M. D. O., Pereira, J. C., Grellet, M., 2000, Adaptive

Estimation of Residue Signal for Voice Pathology

Diagnosis, IEEE Trans. Biomedical Eng., Vol. 47, No.

1, Jan. 2000.

Pulakka H., 2005, Analysis of Human Voice Production

Using Inverse Filtering, High-Speed Imaging, and

Electroglottograph,. University of Technology

Helsinki.

Software Praat, http://www.fon.hum.uva.nl/praat/,

University of Amsterdam.

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence