# Analysis and Detection of Pathological Voice using Glottal Source Features

Sudarsana Reddy Kadiri and Paavo Alku S.R. Kadiri and P. Alku are with the
Dept. of Signal Processing and Acoustics, Aalto University, Finland, E-mail:
<EMAIL_ADDRESS>This study was funded by the Academy
of Finland (project no. 284671, 312490).

###### Abstract

Automatic detection of voice pathology enables objective assessment and
earlier intervention for the diagnosis. This study provides a systematic
analysis of glottal source features and investigates their effectiveness in
voice pathology detection. Glottal source features are extracted using glottal
flows estimated with the quasi-closed phase (QCP) glottal inverse filtering
method, using approximate glottal source signals computed with the zero
frequency filtering (ZFF) method, and using acoustic voice signals directly.
In addition, we propose to derive mel-frequency cepstral coefficients (MFCCs)
from the glottal source waveforms computed by QCP and ZFF to effectively
capture the variations in glottal source spectra of pathological voice.
Experiments were carried out using two databases, the Hospital Universitario
Príncipe de Asturias (HUPA) database and the Saarbrücken Voice Disorders (SVD)
database. Analysis of features revealed that the glottal source contains
information that discriminates normal and pathological voice. Pathology
detection experiments were carried out using support vector machine (SVM).
From the detection experiments it was observed that the performance achieved
with the studied glottal source features is comparable or better than that of
conventional MFCCs and perceptual linear prediction (PLP) features. The best
detection performance was achieved when the glottal source features were
combined with the conventional MFCCs and PLP features, which indicates the
complementary nature of the features.

###### Index Terms:

Speech analysis, Pathological voice, Pathology detection, Glottal source
features, Glottal flow waveform, Glottal inverse filtering.

##  I Introduction

Speech is produced by exciting a time-varying vocal tract system that consists
of various articulators (such as the tongue, jaw, lips) by a time-varying
excitation signal. The main purpose of speech in communication is to convey a
linguistic message. Apart from linguistic content, speech also contains rich
information about the language and dialect as well as about the speaker’s
gender, age, emotions and state of health. This work studies pathological
voice and compares it to normal voice using both analysis and detection (i.e.,
normal vs. pathological). Voice pathologies arise due to infections,
physiological and psychogenic causes and due to vocal misuse that is prevalent
in professions such as singers, teachers, and customer service representatives
[1, 2]. Automatic detection of voice pathology enables an objective assessment
and an early intervention for the diagnosis. A typical voice pathology
detection system consists of two main stages: the first stage is the
representation of the input acoustic speech signal (i.e., feature extraction)
and the second stage is the classifier (i.e., normal vs. pathological
decision). The main focus of the current study is on the first stage.

Feature sets used for voice pathology detection can be broadly classified into
the following three categories [3, 4]: (1) perturbation measures, (2) spectral
and cepstral measures, and (3) complexity measures. Perturbation measures
capture the presence of aperiodicity and aspiration noise in the acoustic
speech signal that occur due to irregular movement of the vocal folds and
incomplete glottal closure. The popular parameters in this category are jitter
and shimmer [5, 6, 7, 8, 9, 10, 11]. Jitter measures the short-term
perturbations of the fundamental frequency ($F_{0}$) and shimmer measures the
short-term perturbations in amplitude [5, 12]. Several variations of jitter
(such as relative jitter, relative jitter average perturbation and jitter
five-point period perturbation quotient) and shimmer (such as absolute
shimmer, relative shimmer and shimmer three-point amplitude perturbation
quotient) have been used for voice pathology detection [12, 13]. The
estimation of these features depends on $F_{0}$, but accurate estimation of
$F_{0}$ is known to be difficult in pathological voice [14, 15]. Even though
many previous studies have investigated jitter and shimmer features, it is
worth observing that these features are not included in the feature set
recommended by the American Speech-Language-Hearing Association due to their
lack of clinical voice utility [16]. For more details of the recommended
acoustic measures, see Table 2 in [16]. Other popular perturbation measures
that quantify the presence of aspiration noise include the harmonics-to-noise
ratio (HNR) [17, 18], normalized noise entropy (NNE) [19], and glottal-to-
noise excitation (GNE) ratio [20, 21, 22]. HNR is defined as the ratio between
the harmonic component energy and the noise component energy. NNE is the ratio
between the energy of noise and the total energy of the signal. GNE measures
the correlation between Hilbert envelopes in different frequency bands of the
acoustic speech signal.

Measures derived from spectrum and cepstrum have been used extensively for
voice pathology detection because these methods are typically easy to compute
and do not need the estimation of $F_{0}$ [23, 24, 15]. The popular features
in this category are mel-frequency cepstral coefficients (MFCCs) [15, 25, 4]
that utilize the principles of human auditory processing in the mel-scale and
the decorrelating property of cepstrum. In addition, linear predictive
cepstral coefficients (LPCCs) [26, 27, 21] and perceptual linear prediction
(PLP) [28, 4] coefficients have been used in voice pathology detection. LPCCs
capture the vocal tract system characteristics. PLP features are based on the
modelling of the human auditory system using the Bark scale, equal loudness-
level curve and intensity-to-loudness conversion [29]. Another popular feature
in this category is the cepstral peak prominence (CPP) [30, 31]. A larger CPP
value indicates a more prominent periodic structure of the signal. A variant
of CPP that has been proposed by smoothing the cepstrum and the corresponding
parameter is referred to as the smoothed CPP [32]. Studies [33, 34] have
additionally used the average spectral energies in low-frequency and high-
frequency bands. Features derived from time-frequency decomposition techniques
such as adaptive time-frequency transform [35, 36], wavelet transform [37, 38,
39, 40], modulation spectrum [41, 42, 43] and empirical mode decomposition
[44] have also been investigated for voice pathology detection.

Complexity measures have been proposed to capture properties such as
aperiodicity, non-linearity and non-stationarity present in the signal through
estimators based on non-linear dynamic analysis [45, 5, 46, 47, 48, 49, 50].
It is known that nonlinear phenomena are common in natural physiological
systems such as speech production. Non-linear dynamic analysis characterizes
the dynamic changes in voice pathologies that occur due to irregular and
improper movement of the vocal folds. The popular parameters in this category
are computed using the fractal dimension or the correlation dimension [45, 51,
52, 53, 28]. The complex measures investigated in several studies consist of
the following: the largest Lyapunov exponent, the recurrence period density
entropy, Hurst exponent, detrended fluctuation analysis, approximate entropy,
sample entropy, modified sample entropy, Gaussian kernel sample entropy, fuzzy
entropy, hidden Markov model (HMM) entropy and Shannon HMM entropy [54, 55,
38, 39]. These features capture the dynamic variants/invariants, long-range
correlations, regularity or predictability information present in the signal.

It should be noted that the estimation of perturbation features and complexity
features depends on the precise estimation of $F_{0}$ and the selection of the
appropriate window length [14, 56]. On the other hand, extraction of spectral
or cepstral features does not depend on $F_{0}$. In [13] and [57], it was
found that voice pathology detection performance with spectral features (such
as MFCCs and PLPs) alone is comparable or better than that given by
perturbation and complexity features in sustained vowels and continuous
speech. More details of the studies on pathological voice and various features
used for voice pathology detection can be found in recent review articles [4,
13]. Regarding classifiers, several known techniques, such as kNN, GMM, LDA,
HMM, ANN, CNN and SVM, have been used for pathological voice [58, 27, 59, 60,
61, 40, 62, 25, 63, 64, 65, 66]. Among the different classifiers, SVM has been
found to be the most suitable classifier for voice pathology detection [67].
More details of various classifiers and machine learning techniques used for
voice pathology detection can be found in the recent review published in [67].

Since voice pathologies affect the speech production mechanism, both the
glottal source and the vocal tract system need to be represented and
parameterized effectively in the analysis and detection of voice pathology.
Existing studies have captured the vocal tract system characteristics
effectively by deriving spectral or cepstral features such as MFCCs and PLPs.
However, there is little previous research on the systematic investigation of
glottal source features for the analysis and detection of voice pathologies.
In the few studies [68, 69, 70, 71], authors have mainly exploited features
that capture the specific glottal source characteristics such as HNR, GNE and
spectral energies in low-frequency and high-frequency bands of the glottal
source.

The current study presents a systematic analysis of glottal source features in
normal and pathological voice and investigates their effectiveness in voice
pathology detection. The glottal source features are derived from the glottal
flow waveforms estimated using the quasi-closed phase (QCP) glottal inverse
filtering method [72] and from the approximate glottal source signals computed
by the zero frequency filtering (ZFF) method [73]. The glottal flow signals
estimated using QCP are parameterized in terms of time-domain and frequency-
domain features [74, 75]. The features derived from the ZFF method consist of
the strength of excitation (SoE), energy of excitation (EoE), loudness measure
and ZFF signal energy [76]. In addition to parameterizing glottal source
waveforms computed by QCP and ZFF, we also use features which are derived
directly from acoustic speech signals and which capture the specific property
of the glottal source. These features are the maximum dispersion quotient
(MDQ) [77], peak slope (PS) [78], cepstral peak prominence (CPP) [30], and Rd
shape parameter [79, 80]. Further, we propose to derive MFCCs from the glottal
source waveforms to effectively capture glottal source variations in
pathological voice. In total, this results in five sets of glottal source
features as follows.

  * •

Time-domain and frequency-domain features derived from the glottal source
waveforms estimated with the QCP method

  * •

Features derived from the approximate glottal source waveforms computed by the
ZFF method

  * •

Features which are derived directly from acoustic voice signals and which
capture the specific property of the glottal source

  * •

MFCCs derived from the glottal flow waveforms estimated with the QCP method

  * •

MFCCs derived from the approximate glottal source waveforms given by the ZFF
method

Voice pathology detection experiments were carried out using two databases,
the Hospital Universitario Príncipe de Asturias (HUPA) database [81, 82] and
the Saarbrücken Voice Disorders (SVD) database [83, 84] that are considered
the most reliable and standard databases for voice pathology detection [4, 13,
85]. We did not utilize the popular MEEI database because it suffers from the
problems such as having different recording conditions between healthy and
pathological voices (see, e.g., [47, 21, 3]). The conventional MFCC and PLP
features, which were shown to be effective for voice pathology detection in
[13], are used as the baseline features. Additionally, the complementary
nature of the glottal source features is demonstrated, when the glottal source
features are combined with the conventional MFCC and PLP features.

The paper is organized as follows. Section II describes the two signal
processing methods, QCP and ZFF, for deriving glottal source waveforms. The
extraction of the glottal source features is discussed in Section III. Section
IV presents the systematic analysis of the glottal source features for normal
and pathological voice. Section V describes the extraction of MFCCs from the
glottal source waveforms. Experimental protocol is discussed in Section VI,
which includes the pathology databases, parameters used for feature
extraction, baseline features used for comparison, details of the classifier
and evaluation metrics. Results and discussion of the detection experiments
are presented in Section VII. Finally, Section VIII summarizes the paper.

##  II Signal Processing Methods used for Deriving Glottal Source Waveforms

This section describes the two signal processing methods used in the present
study, the QCP glottal inverse filtering method [72] and the ZFF method [73],
for the estimation of glottal source waveforms. It should be noted that QCP is
based on the source-filter model of speech production but ZFF does not use the
source-filter model. Hence, these two methods are expected to capture distinct
information.

###  II-A The quasi-closed phase (QCP) method

The QCP method [72] is a recently proposed glottal inverse filtering method
for the automatic estimation of the glottal source waveform from speech. The
method is based on the principles of closed phase (CP) [86] analysis which
estimates the vocal tract model from few speech samples located in the CP of
the glottal cycle using linear prediction (LP) analysis. In contrast to the CP
method, QCP takes advantage of all the speech samples of the analysis frame in
computing the vocal tract model. This is carried out using weighted linear
prediction (WLP) analysis with the attenuated main excitation (AME) [87]
waveform as the weighting function. The AME function is designed using glottal
closure instants (GCIs) and the function attenuates the contribution of the
open phase samples in the computation of the acoustic speech signal’s
covariance or autocorrelation function. This operation results in better
estimates of the vocal tract transfer function $V(z)$. Finally, the estimate
of the glottal flow waveform is obtained by inverse filtering the input
acoustic speech signal with the vocal tract transfer function $V(z)$. The QCP
method was shown to be better than four existing inverse filtering methods in
the estimation of the glottal flow from modal and non-modal types of phonation
[72]. This justifies the usage of QCP as a glottal inverse filtering method in
the present study. A block diagram describing the steps involved in the QCP
method is shown in Fig. 1.

Figure 1: Block diagram of the QCP method.

###  II-B The zero frequency filtering (ZFF) method

The ZFF method was proposed in [73] based on the fact that the effect of an
impulse-like excitation (that occurs at the instant of glottal closure) is
present throughout the spectrum including the zero frequency, while the vocal
tract characteristics are mostly reflected in resonances at much higher
frequencies. In this method, the acoustic speech signal is passed through a
cascade of two zero frequency resonators and the resulting signal is
equivalent to integration of the signal four times. Hence, the output grows or
decays as a polynomial function of time. The trend is removed by subtracting
the local mean computed over the average pitch period at each sample and the
resulting output signal is referred as the zero frequency filtered (ZFF)
signal. In this study, we consider the ZFF signal as an approximate glottal
source waveform.

The following steps are involved to derive the ZFF signal:

  1. 1.

The acoustic voice signal ($s[n]$) is first differentiated as follows to
remove any low-frequency trend

$\displaystyle x[n]=s[n]-s[n-1].$ (1)

  2. 2.

The differentiated signal is passed through a cascade of two zero frequency
resonators (pair of poles on the unit circle along the positive real axis in
the $z$-plane). This filtering can be expressed as follows

$\displaystyle y_{o}[n]=\sum_{k=1}^{4}{a_{k}y_{o}[n-k]}+x[n],$ (2)

where $a_{1}=+4$, $a_{2}=-6$, $a_{3}=+4$, $a_{4}=-1$. The resulting signal
$y_{o}[n]$ is equivalent to integration (or cumulative sum in the discrete-
time domain) of the acoustic voice signal four times, hence it approximately
grows or decays as a polynomial function of time.

  3. 3.

The trend in $y_{o}[n]$ is removed by subtracting the local mean computed over
the average pitch period (derived using autocorrelation) at each sample. The
resulting signal ($y[n]$) is called the ZFF signal and is computed as follows

$\displaystyle y[n]=y_{o}[n]-\frac{1}{2N+1}\sum_{i=-N}^{N}{y_{o}[n+i]},$ (3)

where $2N+1$ corresponds to the number of samples in the window used for trend
removal.

The ZFF signal is used to derive the glottal source characteristics [73]. The
positive-to-negative zero-crossings (PNZCs) correspond to GCIs (or epochs) by
considering the negative polarity of the signal [73, 88]. Let us denote epochs
by ${\cal{E}}=\\{e_{1},e_{2},...,e_{M}\\}$, where $M$ is the number of epochs.
The time duration between any two adjacent epochs gives the instantaneous
fundamental period ($T_{0}[k]$), and its reciprocal gives the instantaneous
fundamental frequency ($F_{0}[k]$), i.e.,

$\displaystyle T_{0}[k]$ $\displaystyle=$
$\displaystyle\frac{(e_{k}-e_{k-1})}{f_{s}},\qquad k=2,3,...,M,$ (4)
$\displaystyle F_{0}[k]$ $\displaystyle=$
$\displaystyle\frac{1}{T_{0}[k]}=\frac{f_{s}}{(e_{k}-e_{k-1})},\qquad
k=2,3,...,M,$ (5)

where $f_{s}$ is the sampling frequency.

Another interesting property of the ZFF signal is that the slope of the signal
around each PNZC is proportional to the rate of closure of the vocal folds as
measured using differentiated electroglottography (EGG) signals at the
instants of glottal closure. A block diagram describing the steps involved in
the ZFF method is shown in Fig. 2.

Figure 2: Block diagram of the ZFF method.

To illustrate the glottal source waveforms computed by QCP and ZFF, a segment
of voiced speech along with the simultaneously recorded EGG signal from the
CMU ARCTIC database [89] is used. Fig. 3(a) and Fig. 3(b) show the acoustic
speech signal and the differentiated EGG, respectively. Glottal source
waveforms computed by QCP and ZFF are shown in Fig. 3(c) and Fig. 3(d),
respectively.

Time (sec)

Figure 3: Illustration of glottal source waveforms derived using the QCP and
ZFF methods: (a) acoustic speech signal, (b) differentiated EGG signal, (c)
glottal source waveform estimated by QCP, and (d) approximate glottal source
waveform estimated by ZFF (reversed in polarity for visualization purpose).

##  III Extraction of Glottal Source Features

This section describes the extraction of features from the glottal source
waveforms computed using QCP and ZFF. In addition, the section explains the
extraction of the glottal source features that are computed directly from the
acoustic voice signal and that capture specific properties of the glottal
source.

###  III-A Glottal source features derived using the QCP method

In order to represent the glottal flow waveform in a compact form, different
methods have been developed for parameterization and they can be grouped into
two categories: time-domain and frequency-domain glottal features (also called
glottal parameters).

####  III-A1 Time-domain glottal features

Time-domain glottal flow signals can be parameterized using time-based and
amplitude-based features [74, 75]. In the case of time-based features, the
most classical approach is to compute time-duration ratios between the
different phases (closed phase, opening phase, and closing phase) of the
glottal source waveform in a glottal cycle. The measures are defined by
extracting critical time instants (such as the instant of glottal closure,
primary and secondary glottal opening, the instant of minimum and maximum
glottal flow) from the glottal source waveform. In the case of amplitude-based
features (amplitude quotient [90, 91] and normalized amplitude quotient [92]),
the amplitude of the glottal flow and its derivative are used [93, 90, 92].
The normalized amplitude quotient has been shown to be a strong correlate of
the closing quotient, and it has been extensively used in analyzing voice
quality [92]. Extraction of critical time instants is often difficult and to
overcome this, sometimes time-based features are computed by replacing the
true closure and opening instants by the time instants when the glottal flow
crosses a level, which is set to a value between the maximum and minimum
amplitude of glottal flow in a glottal cycle [75].

####  III-A2 Frequency-domain glottal features

While the computation of time-domain features from the glottal source waveform
is straightforward, these features are affected by distortions such as formant
ripple due to incomplete canceling of formants by the inverse filter [75]. In
such cases, it is useful to derive frequency-domain features for the glottal
source waveform. Frequency-domain features are computed from the spectrum of
the glottal source and they essentially measure the slope of the spectrum.
Several studies have quantified the spectral slope of the glottal source by
utilizing the level of $F_{0}$ and its harmonics. The most widely used
features are the amplitude difference between $F_{0}$ and the first harmonic
(H1-H2) [94], the harmonic richness factor (HRF) [95], and the parabolic
spectral parameter (PSP) [96]. HRF is the ratio between the sum of the
amplitudes of the harmonics above $F_{0}$ and the amplitude of $F_{0}$. PSP is
derived by fitting a parabola to the low frequencies of the glottal flow
spectrum [96].

A total of 12 glottal features (9 time-domain and 3 frequency-domain features)
defined in [74] are used in this study to characterize the glottal flow
waveforms estimated by the QCP glottal inverse filtering method. These
features are extracted using the APARAT Toolbox [74] and they are listed in
Table I.

TABLE I: Time-domain and frequency-domain glottal features derived from glottal flows estimated by QCP. 1.2 |  Time-domain features
---|---
OQ1 |  Open quotient, calculated from the primary glottal opening
OQ2 |  Open quotient, calculated from the secondary glottal opening
NAQ |  Normalized amplitude quotient
AQ |  Amplitude quotient
ClQ |  Closing quotient
OQa |  Open quotient, derived from the LF model
QoQ |  Quasi-open quotient
SQ1 |  Speed quotient, calculated from the primary glottal opening
SQ2 |  Speed quotient, calculated from the secondary glottal opening
|  Frequency-domain features
H1-H2 |  Amplitude difference between the first two glottal harmonics
PSP |  Parabolic spectral parameter
HRF |  Harmonic richness factor

###  III-B Glottal source features derived using the ZFF method

From the ZFF method, the following glottal source features are extracted: the
strength of excitation (SoE), energy of excitation (EoE), loudness measure and
ZFF signal energy. These features have been shown to be useful for
discriminating phonation types and emotions in [76, 97].

The four ZFF-based parameters are computed as follows.

####  III-B1 Strength of excitation (SoE)

The slope of the ZFF signal around each PNZC corresponds to the SoE, which is
proportional to the rate of closure of the vocal folds [97]. A measure of SoE
around the GCI is given by

$SoE=|y[e_{k}+1]-y[e_{k}-1]|,\qquad k=1,2,...,M.$ (6)

where $y[n]$ is the ZFF signal (Eq. 3).

####  III-B2 Energy of excitation ($EoE$)

The $EoE$ feature is computed from the samples of the Hilbert envelope
($h_{e}[i]$) of the LP residual over a 1-ms region around each GCI. This
feature, defined below in Eq. 7, has been shown to measure vocal effort [97].

$\displaystyle EoE=\frac{1}{2K+1}\sum_{i=-K}^{K}{h_{e}^{2}[i]},$ (7)

where 2K+1 corresponds to the number of samples in the 1-ms window.

####  III-B3 Loudness measure

The loudness measure captures the abruptness of glottal closure [97], and it
is defined according to Eq. 8 as the ratio between the standard deviation
($\sigma$) and mean ($\mu$) of the samples of the LP residual’s Hilbert
envelope in a 1-ms region around GCI.

$\displaystyle Loudness~{}measure=\frac{\sigma}{\mu}.$ (8)

####  III-B4 ZFF signal energy ($v_{zff}[n]$)

The energy of the ZFF signal is given by

$\displaystyle v_{zff}[n]=\frac{1}{L}\sum_{i=-L/2}^{L/2}{y^{2}[n+i]},$ (9)

where $y[n]$ is the ZFF signal. The energy of the ZFF signal at GCI is used in
this study.

The steps involved in the extraction of glottal source features from the ZFF
method are shown in the schematic block diagram in Fig. 4.

---
Figure 4: Schematic block diagram for the extraction of glottal source
features using the ZFF method.

###  III-C Glottal source features derived directly from acoustic voice
signals

The following four parameters, which capture the specific property of the
glottal source, are computed directly from acoustic voice signals without
computing the glottal source waveform.

####  III-C1 Maximum dispersion quotient (MDQ)

The MDQ parameter captures the abruptness of closure of the vocal folds [77].
This parameter measures the dispersion in the LP residual around GCI. Here,
wavelet decomposition is carried out for the LP residual. Within a search
interval near the GCI of the decomposed signals, the distance of the maxima
locations to the given GCI is measured. The average of these distances
normalized to the pitch period is referred to as MDQ.

####  III-C2 Peak slope (PS)

The PS parameter [78] captures the spectral slope of the glottal source. The
method involves computing a wavelet-based decomposition of the acoustic voice
signal into octave bands and then fitting a regression line to the maximum
amplitudes at the different frequency bands. The slope coefficient of the
fitted regression line is referred to as PS.

####  III-C3 Cepstral peak prominence (CPP)

The CPP parameter measures the amount of periodicity present in the signal
using cepstrum [30]. A high CPP value reflects a more periodic structure in a
signal. Initially this parameter was proposed to characterize the breathiness
of voice signals [98]. CPP measures the difference between the most prominent
cepstral peak (first rahmonic) and the point with the same quefrency on the
regression line through the smoothed cepstrum.

####  III-C4 Rd shape parameter

The Rd shape parameter is based on first presenting the entire glottal flow
waveform using the parametric Liljencrants-Fant (LF) model [93]) and then
presenting the LF pulse using a single parameter. The Rd shape parameter [79,
80] provides a single feature which captures most of the covariation of the LF
parameters. A high value of Rd indicates a more relaxed voice.

##  IV Analysis of Normal and Pathological Voice with Glottal Source Features

This section presents results that were obtained when the glottal source
features described in Section III were used to analyze normal and pathological
voice. The analyses were carried out using the twenty speakers of HUPA
database (details of the database are given in Section VI-A1). The results
obtained are described in feature distributions that are depicted using box
plots. By presenting box plots of the feature distributions, our aim is to
analyze potential differences between different glottal source features in
their discriminability of normal and pathological voice.

###  IV-A Analysis of the glottal source features derived using the QCP method

Figure 5 shows distributions of the glottal source features derived using the
QCP method for normal and pathological voice. The figure shows the nine time-
domain features (rows 1, 2 and 3) and the three frequency-domain features (row
4). It can be seen that the frequency-domain features result in better
discrimination of normal and pathological voice compared to the time-domain
features. In the time-domain features, NAQ discriminates normal and
pathological voice better than the other features. For the open quotients, OQ1
and OQ2 show larger variations in pathological voice compared to normal
speech, and QoQ indicates less discriminability. On the other hand, the LF
model-based open quotient (OQa) shows good discriminability. AQ, ClQ, SQ1 and
SQ2 show in general small differences in distributions between normal and
pathological voice. This may be due to the difficulty in identifying critical
glottal time instants (instant of glottal closure, primary and secondary
glottal opening).

The frequency-domain features show in general better discriminability of
normal and pathological voice compared to the time-domain features. The values
of H1-H2 and PSP are higher in pathological voice compared to normal
indicating that the spectral slope of the glottal source is deeper. On the
other hand, HRF values are lower for pathological voice due to a weaker
harmonic structure (see Fig. 8) in their glottal source spectrum.

Figure 5: Distribution of the glottal source features derived from the QCP
method for normal and pathological voice using box plots. The central mark
indicates the median, and the bottom and top edges of the box indicate the
$25^{th}$ and $75^{th}$ percentiles, respectively. The whiskers on either side
cover all points within 1.5 times the interquartile range, and points beyond
these whiskers are plotted as outliers using the ${}^{\prime}+^{\prime}$
symbol.

###  IV-B Analysis of the glottal source features derived using the ZFF method

Figure 6 shows the distribution of the glottal source features derived using
the ZFF method. It can be seen that all the features show in general good
discriminability of normal and pathological voice. SoE, which measures the
strength of the impulse-like excitation at glottal closure, is lower in
pathology indicating less abrupt closure of the vocal folds compared to normal
speech. EoE, which measures the energy of excitation at the glottal closure
and captures the vocal effort required to produce the voice signal, is also
lower in pathology compared to normal. As pathological voice is produced with
improper and slower glottal closure, the loudness measure values are also
lower. The ZFF signal energy of pathological voice is lower than in normal
voice, similar to EoE.

Figure 6: Distribution of the glottal source features derived from the ZFF
method for normal and pathological voice.The central mark indicates the
median, and the bottom and top edges of the box indicate the $25^{th}$ and
$75^{th}$ percentiles, respectively. The whiskers on either side cover all
points within 1.5 times the interquartile range, and points beyond these
whiskers are plotted as outliers using the ${}^{\prime}+^{\prime}$ symbol.

###  IV-C Analysis of the glottal source features derived from acoustic voice
signals

Figure 7 shows the distribution of the glottal source features derived
directly from acoustic voice signals. It can be seen that all the features are
capable of discriminating normal and pathological voice. The MDQ feature,
which measures the dispersion of the LP residual around glottal closure, is
high in pathology indicating the occurrence of improper glottal closure and
increased aspiration noise. PS, which captures the spectral slope of the
glottal source, is also higher in pathology. This observation is not very
evident from the figure, as the range of the PS values is higher in normal
voice. As pathological voice is produced with a larger amount of aspiration
noise and improper glottal closure, it is similar to normal voice of breathy
phonation. For breathy or relaxed voices, the Rd feature values are high [79,
80]. This observation is evident from the box plot of the Rd feature. The CPP
feature measures the amount of periodicity present in the signal. Because of
improper glottal closure and an increased amount of aspiration noise in
pathological voice, the harmonicity of the glottal source spectrum is weaker
(see Fig. 8). Hence, the CPP values are lower for pathological voice, which is
evident from the box plot.

Figure 7: Distribution of the glottal source features derived directly from
acoustic speech signals for normal and pathological voice. The central mark
indicates the median, and the bottom and top edges of the box indicate the
$25^{th}$ and $75^{th}$ percentiles, respectively. The whiskers on either side
cover all points within 1.5 times the interquartile range, and points beyond
these whiskers are plotted as outliers using the ${}^{\prime}+^{\prime}$
symbol.

##  V Extraction of MFCCs from Glottal Source Waveforms

From the analysis of the glottal source features described in Section IV-A, it
can be concluded that the features derived from the glottal source spectrum
(frequency-domain features) have a better discriminability compared to the
time-domain features. This motivates us to use the entire glottal source
spectrum, instead of a few single features, in voice pathology detection.
Figure 8 shows spectrograms of glottal flow waveforms for normal and
pathological voice estimated using the QCP method. It can be seen that there
are large variations especially in the harmonic structure of the glottal flow
spectra between normal and pathological voice. In order to capture these
variations and to represent them in a compact form, we propose to derive MFCCs
from the spectra of the glottal source waveforms (as in our recent conference
paper [99]). It should be noted that the proposed MFCC feature extraction is
similar to the computation of conventional MFCC features, except that the
proposed approach operates on the glottal source waveform instead of the
acoustic voice signal.

A schematic block diagram of the extraction of MFCCs from the glottal source
waveform given by QCP and ZFF is shown in Fig. 9. The method involves short-
term spectral analysis, where the glottal source waveform is split into
overlapping time-frames and the spectrum of each frame is computed with DFT.
The spectrum is estimated using a 1024-point DFT with Hamming windowing in
25-ms frames with a 5-ms shift. Mel-cepstrum is derived from the mel-scale-
based analysis of the spectrum of the glottal source, followed by logarithm
and discrete cosine transform (DCT). From the entire mel-cepstrum, the first
13 coefficients (including the $0^{th}$ coefficient) are considered for each
frame. The resulting cepstral coefficients are referred as MFCC-QCP and MFCC-
ZFF for the glottal source waveforms computed by QCP and ZFF, respectively.
From static cepstral coefficients, delta and double-delta coefficients are
also computed.

Time (sec)

Figure 8: Illustration of spectrograms of glottal source waveforms estimated
using the QCP method for normal and pathological voice. Figure 9: Extraction
of MFCCs from the glottal source waveforms computed by the QCP and ZFF
methods.

##  VI Experimental Protocol

This section describes the databases, the feature sets used in voice pathology
detection including the baseline features, the classifier and the evaluation
metrics.

###  VI-A Databases of pathological voice

Two databases containing normal and pathological voice are used in this study.
These databases are the Hospital Universitario Príncipe de Asturias (HUPA)
database [81, 82] and the Saarbrücken Voice Disorders (SVD) database [83, 84].

####  VI-A1 The HUPA database

This database was recorded at the Príncipe de Asturias hospital in Alcalá de
Henares, Madrid, Spain [81, 82]. The dataset contains sustained phonations of
the vowel /a/ by 439 adult Spanish speakers (239 healthy and 200
pathological). Originally, the data was recorded with a sampling frequency of
50 kHz and later downsampled to 25 kHz. Pathological voices contain a wide
variety of organic pathologies such as nodules, polyps, oedemas and
carcinomas. More details of the database can be found in [81, 82, 100].

####  VI-A2 The SVD database

This database was recorded at the Institut für Phonetik at Saarland University
and the Phoniatry Section of the Caritas Clinic St. Theresia in Saarbrücken,
Germany [83, 84]. The data comprises recordings of sustained phonations of the
vowels /a/, /i/ and /u/ in normal, high and low pitches, as well as with
rising-falling pitch. In addition, the data contains recordings of the
sentence ”Guten Morgen, wie geht es Ihnen?” (”Good morning, how are you?”).
The dataset was recorded from 2225 German speakers, of which 869 are healthy
and 1356 are pathological. The database contains 71 different pathologies
including both functional and organic pathologies. The data was recorded with
a sampling frequency of 50 kHz. As in [13], in this study we use the vowels
/a/, /i/ and /u/, produced using normal pitch, and the running speech after
removing the samples with a lower dynamic range, samples which are recorded
after voice therapy and surgical intervention. This procedure resulted in data
of 1518 speakers, of which 661 are healthy and 857 are pathological. More
details of the database can be found in [83, 84].

###  VI-B The proposed glottal source feature sets and parameters used for
feature extraction

In total, five sets of glottal source features are investigated as listed
below:

  * •

Time-domain (OQ1, OQ2, NAQ, ClQ, SQ1, SQ2, AQ, QoQ, OQa) and frequency-domain
(H1-H2, PSP, HRF) features derived from the glottal source waveforms computed
by the QCP method. These features are extracted for every glottal cycle and
QCP analysis is carried out in Hamming-windowed 25-ms frames with a 5-ms
shift.

  * •

Features derived from the approximate glottal source signals computed by the
ZFF method (SoE, EoE, loudness measure, ZFF energy). All these features are
computed around GCIs. EoE and loudness measure are computed from the samples
of the Hilbert envelope of the LP residual (computed with $12^{th}$ order)
over a 1-ms region around each GCI.

  * •

Features that capture the specific property of the glottal source computed
directly from acoustic voice signals without computing the glottal source
waveform (MDQ, PS, CPP, Rd). All these features are computed in 25-ms Hamming-
windowed frames with a 5-ms shift.

  * •

MFCC-QCP is computed from the glottal flow waveforms estimated by QCP in 25-ms
Hamming windowed frames with a 5-ms shift. First 13 static cepstral
coefficients and their delta & double-delta coefficients are computed yielding
a 39-dimensional feature vector.

  * •

MFCC-ZFF is computed from the approximate glottal source waveforms given by
ZFF using 25-ms Hamming windowed frames with a 5-ms shift. Here also, static
coefficients and their delta & double-delta coefficients are computed yielding
a 39-dimensional feature vector.

###  VI-C Baseline features used for comparison

We consider conventional MFCC and PLP features for comparison as they were
shown in [13] to provide good discrimination between normal and pathological
voice.

####  VI-C1 Mel-frequency cepstral coefficients (MFCCs)

Conventional MFCC features were computed using 25-ms Hamming-windowed frames
with a 5-ms shift. The first 13 cepstral coefficients (including the $0^{th}$
coefficient) and their delta & double-delta coefficients were computed
yielding a 39-dimensional feature vector.

####  VI-C2 Perceptual linear prediction (PLP) coefficients

Conventional PLP features were computed using 25-ms Hamming-windowed frames
with a 5-ms shift. The first 13 cepstral coefficients (including the $0^{th}$
coefficient) and their delta & double-delta coefficients were computed
yielding a 39-dimensional feature vector.

###  VI-D Classifier

The most popular classifier for voice pathology detection is support vector
machine (SVM). In the current study, we use SVM with a radial basis function
(RBF) kernel. Experiments were conducted with 20-fold cross-validation, where
the data was partitioned randomly into 20 equal portions. One fold was held
out to be used for testing with the remaining nineteen folds for training. The
training data were z-score normalized and the testing data were normalized by
subtracting the mean and dividing by the standard deviation of the training
sets for each feature. Evaluation metrics were saved in each fold and this
process was repeated for each of the 20 folds. Finally, the evaluation metrics
were averaged over the 20 folds for evaluation.

###  VI-E Evaluation metrics

Standard performance metrics for a binary classifier are considered for each
one of the aforementioned feature sets [13, 101]. Therefore, the following
metrics are used: accuracy (ACC), sensitivity (SE), specificity (SP), area
under the receiver operating characteristic curve (AUC), and equal error rate
(EER). For a better performing system, the values of first four metrics should
be higher and the last metric should be lower.

##  VII Pathology Detection Experiments

Pathology detection experiments were carried out using the SVM classifier with
the individual feature sets described in Sections VI-B and VI-C as well as
with combinations of the feature sets to analyze the complementary information
among the features. In the combination of features, the complementary
information among the glottal source feature sets and complementary
information with the existing spectral features was also investigated. In
total, 12 feature sets were investigated, out of which seven were individual
feature sets (denoted FS-1 to FS-7) and five were combination of feature sets
(denoted FS-8 to FS-12). These feature sets are listed below.

  * •

FS-1: OQ1, OQ2, NAQ, ClQ, SQ1, SQ2, AQ, QoQ, OQa, H1-H2, PSP and HRF.

  * •

FS-2: SoE, EoE, Loudness, ZFF energy.

  * •

FS-3: MDQ, PS, CPP and Rd.

  * •

FS-4: MFCC-QCP

  * •

FS-5: MFCC-ZFF

  * •

FS-6: Conventional MFCC features.

  * •

FS-7: Conventional PLP features.

  * •

FS-8: Combination of FS-1, FS-2 and FS-3

  * •

FS-9: Combination of FS-4 and FS-5.

  * •

FS-10: Combination of FS-1, FS-2, FS-3, FS-4 and FS-5 (combination of all
glottal source features).

  * •

FS-11: Combination of FS-6 and FS-7 (combination of spectral features i.e.,
MFCC and PLP).

  * •

FS-12: Combination of FS-1, FS-2, FS-3, FS-4, FS-5, FS-6, and FS-7
(combination of all glottal source features and spectral features).

A total of five experiments were carried out: one experiment using the HUPA
dataset (sustained phonation of the vowel /a/) and four experiments using the
SVD dataset (sustained phonation of the vowels /a/, /i/, /u/, and the sentence
sample), and the corresponding results are given in Tables II to VI.

Table II shows the voice pathology detection results computed for the vowel
/a/ in the HUPA database with the individual feature sets (FS-1 to FS-7) and
combination of feature sets (FS-8 to FS-12). From the table, it can be
observed that in the case of individual feature sets, the feature set FS-5
(MFCC-ZFF) provided the best performance in terms of accuracy (72.89%), AUC
(0.78) and EER (0.253). In terms of AUC and EER, the next best feature set was
FS-4 (MFCC-QCP), which provided an AUC of 0.77 and EER of 0.267. The MFCC and
PLP feature sets (FS-6 and FS-7) were also close to the MFCC-QCP features
(FS-4). From the combination of feature sets (FS-8 to FS-12), it can be
clearly seen that there exists an improvement in performance for all the
combinations. This indicates the complementary information among the feature
sets. Further, it is observed that the combination of MFCC-ZFF and MFCC-QCP
(FS-9), and the combination of all glottal source features (FS-10) gave the
highest detection performance. Also, it can be observed that the combination
of conventional MFCC and PLP (FS-11) features showed an improvement in
performance, which indicates the presence of complementary information between
these features. Overall, the best performance was observed when all glottal
source feature sets (FS-1 to FS-5) and conventional MFCC and PLP features
(FS-6 and FS-7) were combined. The combination of all the feature sets (FS-12)
gave an accuracy of 78.37% , AUC of 0.84, and EER of 0.207, which highlights
the complementary nature of the conventional features with the glottal source
features for voice pathology detection.

TABLE II:  Results for the vowel /a/ in the HUPA database with individual
feature sets (FS-1 to FS-7) and combination of feature sets (FS-8 to FS-12).
SE refers to sensitivity, SP refers to specificity, AUC refers to area under
the ROC curve and EER refers to equal error rate.

Feature set | Accuracy [%] | SE | SP | AUC | EER
---|---|---|---|---|---
FS-1 |  68.51$\pm$4.80 | 0.76 | 0.62 | 0.72 | 0.304
FS-2 |  66.89$\pm$3.54 | 0.71 | 0.63 | 0.71 | 0.315
FS-3 |  64.33$\pm$4.92 | 0.78 | 0.55 | 0.71 | 0.322
FS-4 |  70.28$\pm$4.14 | 0.75 | 0.66 | 0.77 | 0.267
FS-5 |  72.89$\pm$4.02 | 0.85 | 0.63 | 0.78 | 0.253
FS-6 |  72.00$\pm$4.36 | 0.80 | 0.63 | 0.73 | 0.279
FS-7 |  72.36$\pm$4.23 | 0.78 | 0.67 | 0.74 | 0.272
FS-8 |  73.74$\pm$4.12 | 0.74 | 0.73 | 0.78 | 0.249
FS-9 |  76.67$\pm$5.01 | 0.81 | 0.72 | 0.79 | 0.241
FS-10 |  76.96$\pm$4.12 | 0.80 | 0.74 | 0.79 | 0.238
FS-11 |  73.68$\pm$4.22 | 0.78 | 0.69 | 0.76 | 0.249
FS-12 |  78.37$\pm$4.18 | 0.80 | 0.77 | 0.84 | 0.207

Tables III, IV and V show the detection results for the vowels /a/, /i/ and
/u/ in the SVD database with the individual feature sets (FS-1 to FS-7) and
combination of feature sets (FS-8 to FS-12). As in the results obtained for
the HUPA database, here also (in the case of individual feature sets FS-1 to
FS-7), the MFCC-ZFF features (FS-5) provided the best performance in terms of
AUC and EER in all vowels. In terms of accuracy, the PLP features gave the
highest performance for all the three vowels. Complementary information among
the features can be observed from the results of feature sets from FS-8 to
FS-12. The combination of all the glottal source features (FS-10) gave the
highest AUC and lowest EER for all the three vowels. Overall, the best
performance was achieved when all the glottal source feature sets were
combined with the conventional MFCC and PLP features (i.e., FS-12). The
combination of all the feature sets gave an AUC of 0.76, 0.75 and 0.73, EER of
0.271, 0.284, and 0.292, and accuracy of 74.32%, 72.29% and 71.45% for the
vowels /a/, /i/ and /u/, respectively. It should be noted that the detection
experiments with the vowel /a/ achieved better results (highest AUC, accuracy
and lowest EER) compared to the vowels /i/ and /u/. This might be due to the
use of wider vocal tract openings in the production of the vowel /a/ which
results in less interaction between the glottal source and the vocal tract
[13]. This observation is also in line with results reported in [13].

TABLE III: Results for the vowel /a/ in the SVD database with individual
feature sets (FS-1 to FS-7) and combination of feature sets (FS-8 to FS-12).
SE refers to sensitivity, SP refers to specificity, AUC refers to area under
the ROC curve and EER refers to equal error rate.

Feature set | Accuracy [%] | SE | SP | AUC | EER
---|---|---|---|---|---
FS-1 |  65.91$\pm$4.82 | 0.70 | 0.62 | 0.68 | 0.341
FS-2 |  66.76$\pm$4.19 | 0.72 | 0.62 | 0.70 | 0.332
FS-3 |  64.11$\pm$4.57 | 0.67 | 0.61 | 0.71 | 0.338
FS-4 |  65.12$\pm$4.28 | 0.68 | 0.63 | 0.72 | 0.329
FS-5 |  66.16$\pm$4.92 | 0.69 | 0.63 | 0.74 | 0.307
FS-6 |  67.25$\pm$4.11 | 0.70 | 0.64 | 0.72 | 0.315
FS-7 |  68.19$\pm$3.84 | 0.70 | 0.65 | 0.73 | 0.320
FS-8 |  68.64$\pm$4.02 | 0.69 | 0.66 | 0.73 | 0.319
FS-9 |  68.12$\pm$4.12 | 0.70 | 0.65 | 0.74 | 0.297
FS-10 |  72.12$\pm$4.35 | 0.73 | 0.69 | 0.75 | 0.286
FS-11 |  69.97$\pm$4.02 | 0.71 | 0.69 | 0.74 | 0.302
FS-12 |  74.32$\pm$3.58 | 0.75 | 0.71 | 0.76 | 0.271

TABLE IV: Results for the vowel /i/ in the SVD database with individual
feature sets (FS-1 to FS-7) and combination of feature sets (FS-8 to FS-12).SE
refers to sensitivity, SP refers to specificity, AUC refers to area under the
ROC curve and EER refers to equal error rate.

Feature set | Accuracy [%] | SE | SP | AUC | EER
---|---|---|---|---|---
FS-1 |  63.57$\pm$4.91 | 0.68 | 0.60 | 0.67 | 0.361
FS-2 |  65.12$\pm$4.73 | 0.70 | 0.60 | 0.67 | 0.353
FS-3 |  63.11$\pm$4.89 | 0.66 | 0.59 | 0.66 | 0.367
FS-4 |  65.51$\pm$4.48 | 0.69 | 0.61 | 0.67 | 0.359
FS-5 |  66.10$\pm$4.16 | 0.70 | 0.62 | 0.70 | 0.322
FS-6 |  66.81$\pm$4.22 | 0.71 | 0.61 | 0.70 | 0.323
FS-7 |  67.79$\pm$4.54 | 0.71 | 0.63 | 0.69 | 0.334
FS-8 |  67.04$\pm$4.63 | 0.69 | 0.64 | 0.70 | 0.326
FS-9 |  67.68$\pm$4.32 | 0.70 | 0.62 | 0.71 | 0.316
FS-10 |  66.91$\pm$4.27 | 0.69 | 0.63 | 0.71 | 0.310
FS-11 |  69.11$\pm$3.97 | 0.72 | 0.64 | 0.70 | 0.324
FS-12 |  72.29$\pm$4.02 | 0.74 | 0.67 | 0.75 | 0.284

TABLE V: Results for the vowel /u/ in the SVD database with individual feature
sets (FS-1 to FS-7) and combination of feature sets (FS-8 to FS-12).SE refers
to sensitivity, SP refers to specificity, AUC refers to area under the ROC
curve and EER refers to equal error rate.

Feature set | Accuracy [%] | SE | SP | AUC | EER
---|---|---|---|---|---
FS-1 |  63.12$\pm$4.22 | 0.65 | 0.61 | 0.66 | 0.361
FS-2 |  63.59$\pm$4.35 | 0.64 | 0.63 | 0.66 | 0.365
FS-3 |  62.11$\pm$4.08 | 0.62 | 0.61 | 0.64 | 0.378
FS-4 |  65.91$\pm$3.94 | 0.69 | 0.62 | 0.67 | 0.345
FS-5 |  65.77$\pm$4.19 | 0.70 | 0.61 | 0.68 | 0.336
FS-6 |  65.49$\pm$4.32 | 0.69 | 0.60 | 0.67 | 0.349
FS-7 |  67.14$\pm$3.92 | 0.71 | 0.62 | 0.68 | 0.343
FS-8 |  64.98$\pm$3.96 | 0.69 | 0.61 | 0.68 | 0.347
FS-9 |  66.89$\pm$3.48 | 0.70 | 0.63 | 0.68 | 0.330
FS-10 |  68.84$\pm$3.67 | 0.71 | 0.65 | 0.71 | 0.319
FS-11 |  68.22$\pm$3.84 | 0.70 | 0.66 | 0.69 | 0.328
FS-12 |  71.45$\pm$3.79 | 0.72 | 0.69 | 0.73 | 0.292

TABLE VI: Results for the sentences in the SVD database with individual
feature sets (FS-1 to FS-7) and combination of feature sets (FS-8 to FS-12).SE
refers to sensitivity, SP refers to specificity, AUC refers to area under the
ROC curve and EER refers to equal error rate.

Feature set | Accuracy [%] | SE | SP | AUC | EER
---|---|---|---|---|---
FS-1 |  68.14$\pm$4.18 | 0.68 | 0.67 | 0.67 | 0.332
FS-2 |  65.28$\pm$4.35 | 0.69 | 0.61 | 0.65 | 0.343
FS-3 |  64.47$\pm$4.05 | 0.62 | 0.66 | 0.65 | 0.345
FS-4 |  65.95$\pm$3.92 | 0.64 | 0.67 | 0.68 | 0.327
FS-5 |  68.92$\pm$3.84 | 0.66 | 0.71 | 0.70 | 0.318
FS-6 |  67.89$\pm$4.20 | 0.65 | 0.69 | 0.68 | 0.331
FS-7 |  67.52$\pm$4.58 | 0.65 | 0.68 | 0.70 | 0.322
FS-8 |  70.81$\pm$3.81 | 0.71 | 0.70 | 0.74 | 0.286
FS-9 |  72.15$\pm$3.98 | 0.69 | 0.73 | 0.73 | 0.307
FS-10 |  73.72$\pm$4.03 | 0.69 | 0.75 | 0.73 | 0.296
FS-11 |  69.93$\pm$3.79 | 0.68 | 0.70 | 0.72 | 0.309
FS-12 |  76.19$\pm$3.62 | 0.72 | 0.78 | 0.78 | 0.262

Table VI shows the results for the continuous speech sentences in the SVD
database with individual feature sets (FS-1 to FS-7) and combination of
feature sets (FS-8 to FS-12). In the case of individual feature sets, MFCC-ZFF
(FS-5) achieved the highest AUC of 0.7 and lowest EER of 0.318. Conventional
MFCCs (FS-6), the proposed MFCC-QCP (FS-4) and MFCC-ZFF (FS-5) had nearly
similar performance. The results of the combination of feature sets (FS-8 to
FS-12) indicate the complementary nature of the feature sets. In the case of
combination of feature sets, 1-dimensional glottal source features
(combination of QCP features, ZFF features and features derived directly from
voice signals) gave the highest AUC of 0.74 and lowest EER of 0.286. Overall,
the best performance was achieved (EER of 0.262, AUC of 0.78 and accuracy of
76.19%) when all the feature sets were combined, indicating the complementary
nature of information of the glottal source features with the existing
conventional spectral features, MFCCs and PLPs.

It is worth noting that there exist studies in the literature [4, 67, 82]
which report detection performance superior to that obtained in this study,
but many of those studies have only included a small portion of the database
and/or limited the analyses to a restricted number of pathologies. It is
observed that the trend in the results reported in this paper are in line with
the results reported in [13, 85].

##  VIII Summary

Glottal source features were studied in this work in the analysis of normal
and pathological voice, and these features were further used in voice
pathology detection. The glottal source features were derived from three
signals: from the glottal flows estimated with the QCP inverse filtering
method, from the approximate source signals computed with the ZFF method and
directly from acoustic voice signals. Analysis of features revealed that
glottal source features help in discriminating normal voice from pathological
voice. Detection experiments were carried out using two databases with
individual glottal source feature sets and with a combination of features.
Experiments showed that on their own the studied glottal source features
provide better discrimination compared to spectral features such as MFCCs and
PLPs features. Also, it was shown that complementary information exists among
the different glottal source features. Further, the combination of the
existing spectral features with the glottal source features resulted in
improved detection performance, indicating the complementary nature of
features.

Motivated by the voice pathology detection performance achieved using glottal
source features, we intend to use these features in the future for the
classification of pathologies and for predicting the level of pathology (i.e.,
quantifying the severity level, for example, as mild, medium, high and very
high), which may be helpful for diagnosis.

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|  Sudarsana Reddy Kadiri was born in Kurnool, Andhra Pradesh, India, in 1990.
He received his Bachelor of Technology degree from Jawaharlal Nehru
Technological University (JNTU), Hyderabad, India, in 2011, with a
specialization in Electronics and Communication Engineering (ECE). He did his
M.S. (Research) between 2011 and 2014, and later converted to Ph.D. at
International Institute of Information Technology, Hyderabad (IIIT-H), India.
He obtained his Ph.D. degree from the Department of ECE, IIIT-H, in 2018. He
was a teaching assistant for several courses at IIIT-H during 2012-2018.
Currently, he is a Postdoctoral researcher with the Department of Signal
Processing and Acoustics at Aalto University, Finland. His research interests
include signal processing, speech analysis, speech synthesis, paralinguistics,
affective computing, voice pathologies, machine learning and auditory
neuroscience.
---|---
|  Paavo Alku received the M.Sc., Lic.Tech., and Dr.Sc.(Tech) degrees from the
Helsinki University of Technology, Espoo, Finland, in 1986, 1988, and 1992,
respectively. He was an Assistant Professor with the Asian Institute of
Technology, Bangkok, Thailand, in 1993, and also with the University of Turku,
Finland, from 1994 to 1999. He is currently a Professor of speech
communication technology with Aalto University, Espoo. His research interests
include analysis and parameterization of speech production, statistical
parametric speech synthesis, spectral modeling of speech, speech enhancement,
and cerebral processing of speech.
---|---