Improved Algorithm for Pathological and Normal Voices Identification

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 1, February 2017, pp. 238~243
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i1.pp238-243  238
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Improved Algorithm for Pathological and Normal Voices
Identification
Brahim Sabir1
, Fatima Rouda2
, Yassine Khazri3
, Bouzekri Touri4
, Mohamed Moussetad5
1,3,5
Physics Department, Faculty of Science Ben M’Sik –Casablanca, Morocco
2
Chemsitry Department, Faculty of Science Ben M’Sik –Casablanca, Morocco
4
Language and Communication Department, Faculty of Science Ben M’Sik –Casablanca, Morocco
Article Info ABSTRACT
Article history:
Received Mar 4, 2016
Revised May 18, 2016
Accepted Jun 4, 2016
There are a lot of papers on automatic classification between normal and
pathological voices, but they have the lack in the degree of severity
estimation of the identified voice disorders. Building a model of pathological
and normal voices identification, that can also evaluate the degree of severity
of the identified voice disorders among students. In the present work, we
present an automatic classifier using acoustical measurements on registered
sustained vowels /a/ and pattern recognition tools based on neural networks.
The training set was done by classifying students’ recorded voices based on
threshold from the literature. We retrieve the pitch, jitter, shimmer and
harmonic-to-noise ratio values of the speech utterance /a/, which constitute
the input vector of the neural network. The degree of severity is estimated to
evaluate how the parameters are far from the standard values based on the
percent of normal and pathological values. In this work, the base data used
for testing the proposed algorithm of the neural network is formed by healthy
and pathological voices from German database of voice disorders. The
performance of the proposed algorithm is evaluated in a term of the accuracy
(97.9%), sensitivity (1.6%), and specificity (95.1%). The classification rate is
90% for normal class and 95% for pathological class.
Keyword:
Classification methods
Communication disorders
Neural networks
Voice disorders
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Brahim Sabir,
Departement of Physics,
Faculty of Science Ben M’Sik,
Casablanca, Morocco.
Email: sabir.brahim@hotmail.com
1. INTRODUCTION
In academic field, among others, voice is considered the main tool of human communication, and it
has been shown to carry much information about the general health and well-being of a student. Thus, voice
disorders cause significant changes in speech and impact particularly student academic results and his overall
activities. Medical methods to assess these voice disorders are either by inspection of vocal folds or by a
physician’s direct audition [1], which is subjective, based on perceptual analysis and both depend on
physician’s experiences.
Acoustic analysis based on instrumental evaluation which comprises acoustic and aerodynamic
measure of normal and pathological voices have become increasingly interesting to researchers because of its
nonintrusive nature and its potential providing quantitative data with reasonable analysis time. Speech
disorder assessment can be made by a comparative analysis between pathological acoustic patterns and the
normal acoustic patterns saved in a database [2]. In voice processing we distinguish three principal
approaches: acoustic, parametric and nonparametric approach and statistical methods.
The first approach consist to compare acoustics parameters between normal and abnormal voices
such as fundamental frequency, jitter, shimmer, harmonic to noise ratio, intensity [3]. The second approach is

IJECE ISSN: 2088-8708 
Improved Algorithm for Pathological and Normal Voices Identification (Brahim Sabir)
239
the parametric and non-parametric for features selection [4], [5]. The classification of voice pathology can be
seen as pattern recognition so statistical methods are an important approach.
This paper is organized as a follow: in second Section is dedicated to relevant acoustic parameters
that differentiate pathological from normal voices, the classification methods are presented in Section 3. The
degree of severity in Section 4, Section 5 deals with the defined threshold in literature to differentiate
pathological and normal voices. The proposed method is presented in Section 5, the results in Section 6, and
the last Section is reserved for the conclusion and future work.
1.1. Relevant acoustic parameters
Analysis of voice signal is performed by the extraction of acoustic parameters using digital signal
processing techniques [6]. However the amount of these parameters is huge to be analyzed, which lead to
define the relevant ones. Many papers identify HNR (harmonic-to-noise ratio), Pitch, shimmer as
relevant [7], [8] to identify pathological voices among normal ones.
Other papers [9], [10] found that jitter, normalized autocorrelation of the residual signal in pitch
lag, shimmer and noise measures like HNR (Harmonic to Noise Ratio), are used to identify pathological
voices. Amplitude perturbation, voice break analysis, subharmonic analysis, and noise related analysis are the
most relevant ones in [9].
And in [11], pitch, jitter, shimmer, Amplitude Perturbation Quotient (APQ), Pitch Perturbation
Quotient (PPQ), Harmonics to Noise Ratio (HNR), pitch perturbation quotient (PPQ), Normalized Noise
Energy (NNE), Voice Turbulence Index (VTI), Soft Phonation Index (SPI), Frequency Amplitude Tremor
(FATR), Glottal to Noise Excitation (GNE), have been seen as relevant ones. There are also many works that
tested the combination of mentioned features [12].
1.2. Classification methods
Various pattern classification methods have been used such as : Gaussian Mixture Models(GMM)
and artificial neural network (ANN) [13], [14], Bidirectional Neural Network (BNN) [15], multilayer
perceptron (MLP) which achieved a classification rate of 96% [16], support vector machine (SVM) [17-19],
Genetic Algorithm (GA) [20], [21], method based on hidden markov model [22], [23]. The use of modulation
spectra, classification based on multilayer network [8].
The correct classification rate obtained in previous researches to distinguish between pathological
and healthy voices varies significantly: 89.1% [24], 91.8% [25], 99.44% [26], 90.1%, 85.3% and 88.2% [27].
However, the comparison among the researches carried out is very complex due to the wide range of
measures, data sets and classifiers employed. Authors reported detection accuracy from 80% to 99%. The
results depend on efficiency of methods, choice of classifiers and characteristics of databases. The best
classification was obtained using nine acoustic measures and achieving an accuracy of 96.5 % [28].
1.3. Degree of severity
Actually, the hard part of pathological voice detection is to discriminate light or moderate
pathological voices from normal subjects. The degree according to the G parameter of the GRBAS scale
proposed by [29]. On this G-based scale, a normal voice is rated as 0, a slight voice disorder as 1, a moderate
voice disorder as 2 and finally, a severe voice disorder as 3.
2. THRESHOLD IN LITERATURE
Based on the literature, we have considered: pitch, jitter, shimmer and harmonic-to-noise ratio
(HNR) as relevant parameters to identify the pathological voices. In order to classify initially the recorded
utterances, we are based on threshold defined in the literature as listed in Table 1 and Table 2.
Table 1. Recommended Values of Pitches for Male and Female Signals ([7])
Mean Pitch Minimum Pitch Maximum Pitch
Female signal
recommended value
225 Hz for adult females, 155 for adult females, 334Hz for adult ,
Male signal
recommended value
adult males 128 Hz , adult males 85 Hz , adult males 196 Hz,

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240
Table 2. Recommended Values to differentiate Pathological and Normal Voices
Praat [8] Teixeira [8]
Jitter ddp% Female signal
recommended value
<=1.04% <=0.66
Male signal recommended
value
<=1.04% <=0.44
Shimmer dda% Female signal
recommended value
<=3.810% <=2.43
value
<=3.810% <=2.01%
HNR (dB) Female signal
recommended value
<20 dB 15.3dB
value
<20 dB 17.3 dB
3. PROPOSED METHOD
The corpus used in this paper is composed of 50 voices of male and 50 voices of females aged 19 to
22 (mean: 20.2). The speech material is obtained by sustained vowel /a/ varies from 3 to 5 seconds (mean 3s).
Among the 50 voices, 25 are normal and 25 are pathological based on initial classification. The signals are
recorded keeping mic 5 cm away from the mouth using a Dictaphone (Sony ICDPX240 4GB).
The record consisted in a 3-4 seconds of sustained sound of the vowel /a/ for each student. The
sampling frequency used for recording these signals was 22.05 kHz, with 16 bit resolution and mono. Praat
software is used to extract the acoustic parameters after transferring the recorded utterance from Dictaphone
to a personal computer Dell (intel ® core ™ i7 CPU M640 @ 2.8 Ghz 2.8 Ghz, 4 Go memory) using
audacity software.
Connect the headphone jack to the line input of the PC with a 3.5 jack cable male / male, then
activates audio recording with audio processing software audacity. An initial classification based on
threshold from the literature, in order to classify the recorded utterances on healthy and pathological. The
technic used to identify pathological voices is ANN (artificial neural network): 4 coefficients are extracted
(pitch, HNR, jitter and shimmer) from the signal, these coefficients are the vector input of the net.
The net is formed by 3 layers and the used algorithm is back propagation algorithm. The activation
function is sigmoid. The proposed algorithm was tested by German database (The sample are from 19 to 22
years old) utterances of /a/. The degree of the severity is evaluated by: Degree of Severity in %= (Measured
value –normal value)/( normal value). Figure 1 shows the normal value is related to the threshold defined in
the literature. Macro steps of the proposed algorithm and Figure 2 shows training of ANN with input vector
(4 acoustic parameters).
Figure 1. Macro Steps of the Proposed Algorithm
Input audio
Extract acoustic parameters:
Pitch, Jitter, Shimmer and HNR
-Initial classification based on threshold from the literature of acoustic
parameters
-Evaluate the degree of severity
-Feed the neural network
-Train the neural network
Test the neural network

IJECE ISSN: 2088-8708 
241
Figure 2. Training of ANN with Input Vector (4 Acoustic Parameters)
4. RESULTS
Description of the dataset as shown in Table 3:
Table 3. Description of the Dataset
Pronounced vowel /a/ Normal pathological
Training number 50 50
Test number 20 20
Correct classification 18 19
Rate of classification 90% 95%
In this work, the testing set we have choose a German database for voice disorder developed by
Putzer in [30] which contain healthy and pathological voice, where each one pronounce vowels [i, a, u] /1-2 s
in wav format at different pitch (low, normal, high). The results obtained in that work indicate a percentage
of correct classification of 90% for normal voices and 95% for pathological voices.
These results prove that the pitch, HNR, jitter and shimmer can be best input parameters for
discrimination and identification of pathological voice using neural network. Also the degree of severity of
the identified pathology was estimated in order to give the clinician clear idea on severity of the
communication disorder. In this process the accuracy, sensitivity and specificity for each threshold were
observed to get the threshold which achieves the best accuracy, and in the same time preserves a high
sensitivity and specificity.
True positive (TP): refer to pathological voices that were classified as pathological by proposed algorithm.
True negative (TN): refer to normal voices that were classified as normal by proposed algorithm.
False positive (FP): refer to normal voices that were incorrectly classified as pathological by proposed
algorithm.
False negative (FN): refer to pathological voices that were incorrectly classified as normal by proposed
algorithm.
1) Specificity = TN/(TN + FP)= 95.1%
2) Sensitivity = TP/(TP + FN)=1.6%
3) Accuracy = (TN + TP)/(TN + FP + TP +FN )=97.9%
Initialization of synaptic weights through each neuron in the hidden layer
Initializing synapse weight bias of each neural layer exit.
Initializing synapse weight of each neuron of the output layer:
Activation function
Function calculates the output ys
Function calculates the error between the observed and desired output
Function calculates the local output layer Gradient
Function lets you choose the maximum error
Function to adjust the weights of the neurons in the output layer
Function calculates the gradient error in the hidden layer
Function to adjust the weights of the hidden layer neurons
Function calculates the global error
Record the weight in the file "poids.txt".
Record the weight of the hidden layer
Add the weight of the output layer

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242
Table 4 shows evaluation of the proposed algorithm.
Table 4. Evaluation of the Proposed Algorithm
5. CONCLUSION
The purpose of this work is to conceive a model to assist the clinicians and the professors to follow
the evolution of the voice disorders among students, based only on acoustic properties of a student’s voice.
The results using the ANN (Artificial neural network) classifier gives 90%for normal class and 95% for
pathological class.
The performance of the proposed algorithm is evaluated in a term of the accuracy (97.9%),
sensitivity(1.6%), and specificity(95.1%). This work can be applied in the field of preventive medicine in
order to achieve early detection of voice pathologies. In addition to that, an estimation of the degree of
severity was proposed. The major advantage of this type of automatic identification tool is a determinism that
is currently lacking in the subjective analysis.
As a possible improvement, system performance can be improved by increasing the corpus
Learning. And as a Future work : identify the type of pathology for each voice disorder( stuttering,
dysphonia…), the study of the continuous speech is a superior objective and an evident next step and
implement the proposed algorithm on a hardware ready to use device as a DSP(Digital signal processing
board) or FPGA (Field Programmable Gate Arrays Board).
ACKNOWLEDGEMENT
The authors fully acknowledged the students Ayoub Ratnane and Abdellah Elhaoui for their
valuable contributions.
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