# Speech glottal flow performance in early stages of Parkinson's disease and multiple systems atrophy

Khalid Daoudi, Farouk Yahaya, Solange Milhé de Saint Victor, Alexandra Foubert-Samier, Margherita Fabbri, Anne Pavy-Le Traon, Olivier Rascol, Clémence Devoucoux, Virginie Woisard, Wassilios G. Meissner

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Khalid Daoudi, Farouk Yahaya, Solange Milhé de Saint Victor, Alexandra Foubert-Samier, Margherita Fabbri, et al.. Speech glottal flow performance in early stages of Parkinson's disease and multiple systems atrophy. 2025. hal-05356075

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Distributed under a Creative Commons Attribution 4.0 International License
Speech glottal flow performance in early stages of Parkinson's disease and multiple systems atrophy

Khalid Daoudi¹*, Farouk Yahaya¹, Solange Milhé de Saint Victor ², Alexandra

Foubert-Samier³,⁴, Margherita Fabbri ⁵, Anne Pavy-Le Traon ⁵, Olivier Rascol ⁵, Clémence

Devoucoux ⁶, Virginie Woisard ⁶, Wassilios G. Meissner 3,4,7

¹INRIA centre at the University of Bordeaux, GeoStat team, France

²ENT Department, University Hospital of Bordeaux, France

³CHU Bordeaux, Service de Neurologie des Maladies Neurodégénératives
IMNc, CRMR AMS, F-33000 Bordeaux, France

⁴Univ. de Bordeaux, CNRS, IMN, UMR 5293, F-33000 Bordeaux, France

⁵MSA Reference Center, Clinical Investigation Center CIC1436,
Dep. Neurosciences & Clinical Pharmacology, University Hospital of Toulouse, France

⁶Voice and deglutition unit, ENT department, University Hospital of Toulouse, France

⁷Dept. Medicine, University of Otago, Christchurch, New Zealand
and New Zealand Brain Research Institute, Christchurch, New Zealand

**Abstract**

The speech phonation subsystem has been widely studied for assessing phonatory impairments in the presence of numerous pathologies, especially neurodegenerative diseases. Perturbation or/and cepstral measures have been the traditional acoustic features for this purpose. However, these measures have the drawback of involving all the speech production subsystems. To isolate the phonatory component, analysis of the glottal flow generated by the vibration of the vocal folds is an appealing alternative. However, despite its significant pertinence, this approach has been little studied. In this paper, we conducted the first study of glottal flow performance in the setting of atypical parkinsonian disorders. Our main objective was the identification of distinctive glottal features which can assist in the challenging problem of early differential diagnosis between Parkinson's disease (PD) and the parkinsonian variant of multiple systems atrophy (MSA-P). In addition to the traditional sustained phonation task, we analyzed short vowel production in diadochokinesis (DDK) tasks, using a classical method for the estimation and parameterization of the glottal flow. The results showed that the DDK task was valuable towards revealing MSA-P specific glottal disruptions. Moreover, contrarily to other studies, our results suggested that early-stage PD patients do not manifest glottal flow impairment.

**Keywords:** Dysarthria, Dysphonia, Parkinson's disease, Multiple systems atrophy, Speech glottal flow, Glottal flow inverse filtering.

<footnote>*Corresponding author: khalid.daoudi@inria.fr</footnote>
# 1. Introduction

Parkinson's disease (PD) is a neurodegenerative disease characterized by the loss of mid-brain dopaminergic neurons and the accumulation of misfolded alpha-synuclein in neurons. Atypical parkinsonian disorders (APD) are a group of rare and progressive neurodegenerative diseases which manifest similar symptoms to PD, but which do not respond well to levodopa treatment in general. Multiple system atrophy (MSA) belongs to the latter group and has a poor prognosis. The difference between PD and MSA is characterized by a more widespread neurodegenerative process for the latter (Poewe et al., 2022). MSA has two forms, the cerebellar variant MSA-C and the parkinsonian variant MSA-P. Clinical diagnosis criteria rely on the presence of symptoms and imaging features, while the definite MSA diagnosis requires postmortem confirmation by a neuropathological examination (Wenning et al., 2022). The similarity between PD and MSA-P symptoms makes very difficult the task of distinguishing between these two diseases, at least in early disease stages (Poewe et al., 2022). Despite recent progresses, especially in brain imaging and analyzing biofluids (Poewe et al., 2022), no biomarker has been validated so far for the differential diagnosis. There exists thus a strong need for such a biomarker to improve the accuracy of diagnosis. An accurate early diagnosis is indeed essential not only for prognostic guidance, but also for therapeutic development (Bendetowicz et al., 2024).

The objective evaluation of dysarthria, a class of motor speech disorders, has gained an ever-growing interest for the development of a digital biomarker based on automatic speech processing to assist in the differential diagnosis between PD and APD. This is motivated by the fact that most PD and APD patients manifest dysarthria from the early disease stages. PD patients develop essentially hypokinetic dysarthria whilst MSA patients develop both hypokinetic and ataxic dysarthria (Darley et al., 1969; Duffy, 2019). Thus, during the last decades, the objective assessment of PD speech has been widely explored (recent reviews can be found in (Moro-Velazquez et al., 2021; Hecker et al., 2022; Xavier et al., 2025; Cao et al., 2025)). Compared to PD, the exploration of the objective assessment of APD speech is more recent (Skodda et al., 2011; Saxena et al., 2014; Huh et al., 2015; Rusz et al., 2015; Miller et al., 2017; Hlavnička et al., 2017; Hlavnicka, 2018; Rusz et al., 2019; Kowalska-Taczanowska et al., 2020; Li et al.; Das et al., 2019; Daoudi et al., 2021; Daoudi et al., 2022, 2024; Das, 2021).

All the speech production subsystems can be affected by dysarthria: respiration, articulation, phonation, timing and prosody (and nasality to a lower extent) (Duffy, 2019). In this paper, we focused on a specific component of the phonation subsystem, the glottal flow (or glottal signal), which is the airflow excitation signal generated by the vocal folds (located in the larynx) under lung pressure (see Figure 1 for an illustration). Phonation in PD speech has been extensively studied (Moro-Velazquez et al., 2021) (and references within). There exists also numerous studies reporting on phonation in MSA speech (Rusz et al., 2019; Daoudi et al., 2022) (and references within). These studies have been mainly based on analysis of sustained phonations where the speaker produces sustained vowels, often the vowel /a/ (Romero-Arias et al., 2023). The latter have been essentially analyzed by perturbation measures such as jitter, shimmer and harmonic to noise ratio (HNR), or/and cepstral measures, such as cepstral peak prominence (CPP) (Fraile and Godino-Llorente, 2014).

Whilst these measures have become standard features, they have some limitations. In
particular, their computation is based on the analysis of time/frequency representations of
the recorded signal which is formed by a contribution of all the production subsystems, except
prosody (Duffy, 2019). Therefore, the measurements are not guaranteed to reflect only the
performance of the phonation subsystem. The analysis of the glottal flow represents thus
an appealing alternative to restrict the analysis to the sole phonatory component generated
by the vocal folds (Drugman et al., 2014). Access to the ground truth of the glottal flow is
technically very challenging. However, it can be estimated from the speech signal using the so
called glottal source inverse filtering (GSIF) (Kadiri et al., 2021). The latter is a field which
has been extensively studied in healthy speech (see (Kadiri et al., 2021) for a recent review),
and one of the most popular and effective methods is the Iterative Adaptive Inverse Filtering
(IAIF) (Alku, 1992). Several features have been proposed to parameterize the (estimated)
glottal flow which can be grouped into time and frequency domain features (Kadiri et al.,
2021).

Compared to perturbation and cepstral measures, the amount of studies reporting on
glottal flow analysis of PD speech, and pathological speech in general, is relatively limited
(Belalcazar-Bolaños et al., 2015, 2016; Hanratty et al., 2016; Corcoran et al., 2019; Gómez-
Rodellar et al., 2020; Álvarez et al., 2020; Narendra and Alku, 2020; Narendra et al., 2021;
Vasquez et al., 2021). As for MSA, no study exists on the behavior of the glottal flow in MSA
speech. More importantly, all these studies but one have analyzed groups of PD patients with
mild to severe disease severity and moderate to long disease duration. This heterogeneity in
demographic data and processing methods has led to led to inconsistent findings. Only one
study dealt exclusively with early-stage PD (Novotny et al., 2020). That study considered a
group of 40 male de-novo untreated PD patients, with a mean disease duration of 1.5 ± 1.3
years, producing sustained phonation of the vowel /a/.

Our goal in this paper was to evaluate the utility/potential of glottal flow performance
as a discriminative cue between PD and MSA-P, in early stages. We first investigated this
performance in the traditional sustained phonation task. Given that (Novotny et al., 2020)
was the only study which used early stage PD in that task, we followed the same procedure
of (Novotny et al., 2020) for the glottal flow analysis which consisted in computing 4 widely
used glottal features on a sustained /a/ (2 time-domain and 2 frequency-domain) that were
individually statistically analyzed. We then investigated the behavior of the glottal flow
on diadochokinesis (DDK) tasks. The latter has been designed and generally dedicated to
evaluate the performance of the articulation subsystem. It has been shown to be one of the
most suitable tasks to discriminate between HC and PD as well as between PD and MSA
(Kent et al., 2021). Surprisingly, the glottal flow of DDK utterances has not been (directly)
studied despite the advantage of offering an appealing complementary perspective to the
traditional analysis of the phonation subsystem using sustained /a/. Indeed, it involves the
repetitive production of (natural) short /a/ in a consonant-vowel articulation context. It thus
offers the possibility of studying the glottal flow of a non-sustained /a/ when the articulation
subsystem is also involved. We thus analyzed the performance of the glottal flow on two
widely used DDK tasks, the Alternating Motion Rate (AMR) which is the repetition of the
syllable /pa/, and the Sequential Motion Rate (SMR) which is the repetition of the syllable
sequence /pataka/.

The paper is organized as follows. The speech database and the analysis tools are de-
scribed in the next section. Results are presented in section 3. A discussion and a conclusion
<figure><img src="image_3.png" /><figcaption>Figure 1: Voice production mechanisms: vocal folds vibrations, vocal tract filtering and lips/nose openings radiation.</figcaption></figure>

are given in section 4 and 5, respectively.

## 2. Methods

### 2.1. Participants

Ninety two French speaking participants were recruited in this study. Thirty four patients (8 females and 26 males) were diagnosed with PD (mean age of 63.7±7.1 and mean symptom duration of 3.9±2.3 years). Twenty seven subjects (13 females and 14 males) were diagnosed with MSA-P (mean age of 63.7±7 and mean symptom duration of 4.3±2.1 years). At the time of the examination, patients were in a stable on-state with dopaminergic medication. Neurologists (specialists of movement disorders) performed the clinical diagnoses based on the Movement Disorder Society UMSARS clinical criteria for MSA (Gilman et al., 2008) and the Movement Disorder Society UPDRS clinical criteria for PD (Hughes et al., 1992). Thirty one healthy controls (HC) with a mean age of 61±8.6 (14 females and 17 males) without any history of communication or neurological disorders were recruited. Otorhinolaryngologists carried out all the recording sessions. Each participant produced several speech tasks including sustained phonation, syllables repetition, isolated pseudo-words, reading a short text and a monologue. These tasks are known to be valuable for the objective assessment of motor speech impairments (Rusz et al., 2021). In this study, we used the data of sustained phonation of the vowel /a/ as well as the AMR and the SMR tasks. The speech signals were recorded with 48 kHz sampling frequency and 16 bit resolution by a headmount condenser microphone (t.bone HC 444 TWS) placed at a distance of approximately 5 cm from the
speaker's mouth. Ethics approval was given prior to recruitment and all participants gave written informed consent.

## 2.2. Acoustic analysis

Figure 2 illustrates an idealized glottal flow waveform and its derivative, with key time instants, durations and amplitudes:

* $T_0$ is the glottal cycle duration (the fundamental frequency $F_0 = 1/T_0$).

* $T_i$ is the duration till the instant of maximal increase in the glottal flow (the maximum $E_i$ of the glottal flow derivative).

* $T_p$ is the duration till the instant of maximum (or peak) flow $fl_{pk}$.

* $T_e$ is the duration till the instant of the maximal vocal tract excitation ($E_e$ in the glottal flow derivative) which corresponds to the glottal closure instant.

* $T_c$ is the duration till the instant of complete glottal closure.

* $T_a$ is the duration of the return phase decay.

To have a baseline for a fair comparison and given that (Novotny et al., 2020) was the only work reporting on early PD, we followed the same procedure for glottal flow estimation, which was based on the pitch synchronous IAIF method (Alku, 1992), and parameterization. The latter consisted in computing the most widely used features:

* The Quasi-Open Quotient (QOQ)

$$QOQ = \frac{T_e - T_i}{T_0 - (T_e - T_i)}$$

* The Normalized Amplitude Quotient (NAQ)

$$NAQ = \frac{fl_{pk}}{E_e \times T_0}$$

* The first and second harmonics difference (H1H2):

$$H1H2 = H1 - H2$$

where H1 is the amplitude of the fundamental frequency and H2 is the amplitude of the second harmonic of the glottal flow spectrum.

* The Harmonic Richness Factor (HRF):

$$HRF = \frac{\sum H_i}{H1}$$

where $H_i$ is the amplitude of the $i^{th}$ harmonic of the glottal flow spectrum.

For the parameterization of the glottal flow on DDK tasks, we additionally applied a voice/unvoiced detection in order to discard unvoiced frames in the computation. As in (Novotny et al., 2020), all the computations were done using the (Matlab) routines of the COVAREP repository (Degottex et al., 2014) which implemented the pitch synchronous IAIF method (Alku, 1992).
<figure><img src="image_4.png" /><figcaption>Figure 2: Idealized glottal flow waveform and its derivative, with key time instants, durations and amplitudes.</figcaption></figure>

## 2.3. Statistical analysis

Group differences were evaluated using ANOVA for Gaussian data and the pairwise
Kruskal–Wallis test for non-Gaussian data. Pairwise correlation was computed using the
Pearson correlation coefficient. The statistical significance threshold was set at a *p*-value
*p* < 0.05. We used a decision tree classifier to differentiate between MSA-P (the positive
label) and PD. The classification performance was evaluated by specificity and sensitivity
using leave-one-out cross-validation (LOOCV).

# 3. Results

## 3.1. *Sustained phonation task*

For each feature computed using sustained phonation of the vowel /a/, Table 1 presents
the mean and standard deviation for each group and the p-values resulting from the statistical
<table><caption>Table 1: Statistical difference between groups using sustained /a/: ns = no significant statistical difference</caption><thead><tr><th></th><th>HC</th><th>PD</th><th>MSA</th><th>HC vs PD</th><th>HC vs MSA-P</th><th>PD vs MSA-P</th></tr><tr><th>Feature</th><th>mean/std</th><th>mean/std</th><th>mean/std</th><th>p-value</th><th>p-value</th><th>p-value</th></tr></thead><tbody><tr><td>QOQ</td><td>0.31/0.08</td><td>0.30/0.09</td><td>0.35/0.10</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>NAQ</td><td>0.08/0.03</td><td>0.09/0.03</td><td>0.10/0.03</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>H1H2</td><td>7.88/4.72</td><td>8.43/4.92</td><td>8.84/3.56</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>HRF</td><td>26.77/7.90</td><td>25.95/6.10</td><td>24.01/6.63</td><td>ns</td><td>ns</td><td>ns</td></tr></tbody></table>

<table><caption>Table 2: Statistical difference between groups using the SMR task: *p < 0.05, **p < 0.01, ns = no significant statistical difference</caption><thead><tr><th></th><th>HC</th><th>PD</th><th>MSA</th><th>HC vs PD</th><th>HC vs MSA-P</th><th>PD vs MSA-P</th></tr><tr><th>Feature</th><th>mean/std</th><th>mean/std</th><th>mean/std</th><th>p-value</th><th>p-value</th><th>p-value</th></tr></thead><tbody><tr><td>QOQ</td><td>0.31/0.06</td><td>0.31/0.07</td><td>0.38/0.13</td><td>ns</td><td>*</td><td>*</td></tr><tr><td>NAQ</td><td>0.08/0.02</td><td>0.09/0.03</td><td>0.10/0.03</td><td>ns</td><td>**</td><td>ns</td></tr><tr><td>H1H2</td><td>5.76/3.61</td><td>7.08/2.83</td><td>8.16/2.33</td><td>ns</td><td>**</td><td>ns</td></tr><tr><td>HRF</td><td>32.00/5.97</td><td>30.69/5.03</td><td>27.15/5.66</td><td>ns</td><td>**</td><td>*</td></tr></tbody></table>

<table><caption>Table 3: Statistical difference between groups using the AMR task: *p < 0.05, **p < 0.01, ns = no significant statistical difference</caption><thead><tr><th></th><th>HC</th><th>PD</th><th>MSA</th><th>HC vs PD</th><th>HC vs MSA-P</th><th>PD vs MSA-P</th></tr><tr><th>Feature</th><th>mean/std</th><th>mean/std</th><th>mean/std</th><th>p-value</th><th>p-value</th><th>p-value</th></tr></thead><tbody><tr><td>QOQ</td><td>0.31/0.08</td><td>0.30/0.08</td><td>0.36/0.11</td><td>ns</td><td>*</td><td>*</td></tr><tr><td>NAQ</td><td>0.09/0.02</td><td>0.09/0.03</td><td>0.11/0.03</td><td>ns</td><td>**</td><td>ns</td></tr><tr><td>H1H2</td><td>6.54/3.85</td><td>7.67/2.85</td><td>8.11/2.75</td><td>ns</td><td>**</td><td>ns</td></tr><tr><td>HRF</td><td>32.58/6.87</td><td>30.57/5.04</td><td>26.98/4.85</td><td>ns</td><td>**</td><td>**</td></tr></tbody></table>

test of difference between group pairs. The results showed that none of the 4 glottal features yielded a significant difference between HC and PD groups (nor between MSA-P and PD). More importantly, no difference between the HC and MSA-P groups was found.

## 3.2. DDK-SMR task

Table 2 presents feature mean and standard deviation along with the results of group difference between group pairs when producing a repetition of the syllable sequence /pataka/. The results revealed that none of the four glottal features yielded a significant difference between HC and PD groups, as in the sustained phonation task. At the same time, all the features yielded a significant difference between HC and MSA-P groups. As for the difference between the PD and MSA-P groups, NAQ and H1H2 did not yield a significant difference whilst QOQ and HRF did.

## 3.3. DDK-AMR task

Table 3 presents feature mean and standard deviations along with the results of group difference between groups pairs when producing a repetition of the syllable /pa/. The statistical behavior of the features was similar to the SMR task.

## 3.4. Bivariate analysis

Correlation analysis revealed that QOQ of AMR and SMR were uncorrelated for HC ($r = 0.29$) and significantly correlated for PD ($r = 0.81$) and MSA-P ($r = 0.72$). The
<figure><img src="image_5.png" /><figcaption>Figure 3: Two-dimensional projection of all patients over QOQ and HRF of SMR</figcaption></figure>

behaviour of HRF was similar, HRF of AMR and SMR were uncorrelated for HC ($r = 0.33$) and significantly correlated for PD ($r = 0.78$) and MSA-P ($r = 0.67$). In both AMR and SMR tasks, QOQ and HRF were correlated for HC and MSA ($|r| < 0.26$) and moderately correlated for PD ($r = -0.47$ and $r = -0.43$, respectively). We thus performed a two-dimensional classification using these two features. Figure 3 displays the biplot of the projection of PD and MSA-P patients over the QOQ and HRF dimensions using SMR. LOOCV classification using a decision tree yielded a good sensitivity (74%) but a low specificity (59%). On the AMR task, the results were similar, a good sensitivity (74%) but a low specificity (53%).

# 4. Discussion

In the first part of this study, we investigated a traditional method of glottal flow perturbation assessment (GFPA) to evaluate the discriminative power between PD and MSA-P of the associated acoustic measures. Following the only study reporting on glottal flow of early PD speech (Novotny et al., 2020), this method consisted in computing and comparing descriptive statistics of four commonly used glottal features extracted from sustained phonation of the vowel /a/. Univariate statistical analysis showed that none of these features yielded significant group differences between HC and PD. In (Novotny et al., 2020), the same behaviour was reported except for HRF which was sensitive to phonatory impairment in early PD. The difference in the behaviour of HRF in (Novotny et al., 2020) and our study might be explained by the difference in demographic data. Indeed, the subjects recruited by (Novotny et al., 2020) were only male and newly diagnosed drug-naïve PD patients. Moreover, the pos-
sibility of atypical parkinsonism cannot be excluded for the latter. A more striking finding in our study is that none of the features revealed group difference between HC and MSA-P (and incidentally between PD and MAS-P). This finding suggested the GFPA method was not capable of reflecting the disruption in glottal flow performance of sustained /a/. The speech task being one of the main components of the GFPA method, we questioned the pertinence of the use of sustained phonation (of /a/) and investigated whether DDK could be a more adequate speech task for evaluating of glottal flow performance.

In both AMR and SMR tasks, the analysis revealed that none of the features yielded group differences between HC and PD. This result suggested that PD patients did not manifest glottal flow impairment in short phonation of /a/ neither. On the other hand, all the features did reveal differences between HC and MSA-P. This indicated that MSA-P patients manifested glottal flow impairment in short /a/ of DDK tasks and that these features could reflect it, as opposed to the sustained phonation task. As for discrimination between PD and MSA-P, NAQ and H1H2 did not reveal group differences whilst QOQ and HRF did, suggesting that the latter features could be useful in the discrimination between PD and MSA-P.

Correlation analysis of these two features yielded interesting observations. Indeed, QOQ of AMR and SMR were uncorrelated for HC. This could be explained by the difference in surrounding plosives of the vowel, causing different vowel timings with a direct impact on QOQ calculation. As for MSA-P, QOQ of AMR and SMR were strongly correlated. This indicated that, for MSA-P patients, the glottal flow disruption in AMR and SMR captured by QOQ was globally insensitive to the surrounding plosive context. The behaviour of HRF, which is computed in the frequency domain, was similar to the time-domain feature QOQ. This indicated that the surrounding plosive context has also a limited impact in frequency domain (of the glottal flow), as captured by HRF. This suggested a stability of the QOQ and HRF measures w.r.t. the DDK task. The non correlation between QOQ and HRF for all groups indicated that combining the two features could improve the discriminative power. A decision tree classification on the QOQ and HRF dimensions, using the AMR and SMR tasks, yielded a good sensitivity but a low specificity. Despite the relatively low value of specificity, it still indicated that these two features conveyed valuable discriminative information which could, for instance, complement discriminative features associated the other speech subsystems to improve the accuracy of discrimination between PD and MSA-P.

Overall, our analysis first revealed that PD patients did neither manifest glottal flow perturbations in the production of sustained nor short /a/. In the study by (Corcoran et al., 2019) (resp. (Kadiri et al., 2021)), all but QOQ (resp. NAQ) did not reflect impairment in sustained /a/ despite that the PD groups included late-stage PD (with presumably high speech severity). We recall that in the study by (Novotny et al., 2020), all but HRF did not reflect impairment in sustained /a/ for newly diagnosed drug-naïve PD patients. Whilst additional studies and data are necessary to reach a potential consensus, our results suggest that, at early disease stages, PD does not affect the glottal flow performance of the vowel /a/ (short and sustained). On the other hand, our analysis revealed that MSA-P patients manifested disruption in the glottal flow performance of the short vowel /a/, when the latter was produced in the (unvoiced)plosive-vowel articulation mode of DDK, reflecting a coordination deficit in the flow transition between the vowel and the plosive(s). From this perspective, this impairment can be viewed as a consequence of ataxic dysarthria which is considered as
the main distinctive dysarthria type between PD and MSA-P. Therefore, QOQ and HRF,
extracted from DDK speech, can be considered as reliable glottal distinctive features that
enrich the set of known ataxic features, such as DDK irregularity and vowel duration (Rusz
et al., 2019; Daoudi et al., 2022).

5. Conclusion

We carried out an evaluation of a traditional method for GFPA of sustained and short /a/,
produced in two DDK tasks, in order to evaluate the potential discriminative power between
early PD and early MSA-P of the associated glottal flow features. Using sustained /a/, the
analysis did not reveal a deviance neither for MSA-P nor for PD. On the other hand, the
analysis revealed that, when producing DKK tasks, the MSA-P group manifested impairment
in glottal flow production whilst the PD group did not. This suggested that this particular
impairment might be specific to MSA-P, in early stages. More precisely, our findings can be
summarized/hypothesized as follows. When using the *traditional* GFPA method (Degottex
et al., 2014) for the estimation and parameterization of the glottal flow :

1. Sustained phonation of the vowel /a/ is not a suitable task for GFPA.

2. DDK is a more suited task.

3. Early-stage PD patients do not exhibit glottal flow impairment in the production of sustained and short /a/ (in DDK tasks).

4. Early-stage MSA-P patients manifest glottal flow impairment in the vowel production in DDK tasks.

5. The glottal features QOQ and HRF, computed on phonation in DDK tasks, are discriminative features between PD and MSA-P.

These findings highlight the relevance of glottal flow analysis of dysarthric speech as
an additional cue to assist in the challenging task of (speech-based) differential diagnosis
between PD and MSA-P, and also by showing its specificity in early disease stages. Glottal
flow analysis should thus not be neglected by the community and should receive at least as
much attention as the traditional phonation analysis. In our future work, we will investigate
other GFPA methods (in addition to the traditional one) and explore additional speech tasks
(such as read and spontaneous speech).

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