Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects

Moro-Velázquez, Laureano; García, Jorge Andrés Gómez; Arias-Londoño, Julián D.; Dehak, Najim; Godino-Llorente, Juan Ignacio

doi:10.1016/j.bspc.2021.102418

Cited by 101 publications

(71 citation statements)

References 135 publications

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“…On the other hand, the amplitude of the Non-Parkinson's Disease subject is uniformly in a decreasing trend. The disorder signal of Parkinson's subjects is the dysphonia and hypokinetic dysarthria that a subject suffers at various stages of PD [ 8 ]. Dysphonia refers to the inability to produce normal phonation due to impaired functioning of the phonatory system.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

Pramanik

Pradhan

Nandy

et al. 2021

Journal of Healthcare Engineering

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This article presents a machine learning approach for Parkinson’s disease detection. Potential multiple acoustic signal features of Parkinson’s and control subjects are ascertained. A collaborated feature bank is created through correlated feature selection, Fisher score feature selection, and mutual information-based feature selection schemes. A detection model on top of the feature bank has been developed using the traditional Naïve Bayes, which proved state of the art. The Naïve Bayes detector on collaborative acoustic features can detect the presence of Parkinson’s magnificently with a detection accuracy of 78.97% and precision of 0.926, under the hold-out cross validation. The collaborative feature bank on Naïve Bayes revealed distinguishable results as compared to many other recently proposed approaches. The simplicity of Naïve Bayes makes the system robust and effective throughout the detection process.

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Section: Introductionmentioning

confidence: 99%

Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

Pramanik

Pradhan

Nandy

et al. 2021

Journal of Healthcare Engineering

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“…An equally wide range of proposals can be found regarding machine learning techniques. Commonly used classifiers that have been used for this application are: Random Forest, Neural Networks or Support Vector Machines, among others [ 18 , 29 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions

et al. 2021

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Background and objective Automatic voice condition analysis systems to detect Parkinson’s disease (PD) are generally based on speech data recorded under acoustically controlled conditions and professional supervision. The performance of these approaches in a free-living scenario is unknown. The aim of this research is to investigate the impact of uncontrolled conditions (realistic acoustic environment and lack of supervision) on the performance of automatic PD detection systems based on speech. Methods A mobile-assisted voice condition analysis system is proposed to aid in the detection of PD using speech. The system is based on a server–client architecture. In the server, feature extraction and machine learning algorithms are designed and implemented to discriminate subjects with PD from healthy ones. The Android app allows patients to submit phonations and physicians to check the complete record of every patient. Six different machine learning classifiers are applied to compare their performance on two different speech databases. One of them is an in-house database (UEX database), collected under professional supervision by using the same Android-based smartphone in the same room, whereas the other one is an age, sex and health-status balanced subset of mPower study for PD, which provides real-world data. By applying identical methodology, single-database experiments have been performed on each database, and also cross-database tests. Cross-validation has been applied to assess generalization performance and hypothesis tests have been used to report statistically significant differences. Results In the single-database experiments, a best accuracy rate of 0.92 (AUC = 0.98) has been obtained on UEX database, while a considerably lower best accuracy rate of 0.71 (AUC = 0.76) has been achieved using the mPower-based database. The cross-database tests provided very degraded accuracy metrics. Conclusion The results clearly show the potential of the proposed system as an aid for general practitioners to conduct triage or an additional tool for neurologists to perform diagnosis. However, due to the performance degradation observed using data from mPower study, semi-controlled conditions are encouraged, i.e., voices recorded at home by the patients themselves following a strict recording protocol and control of the information about patients by the medical doctor at charge.

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“…Most of the research community tends to focus on phonation to assess the patient's ability to force air from the lungs to the vocal folds and make them vibrate to produce sounds [2]. However, it is well known that sustained vowels are an over-simplistic task, which does not include fluctuations in vocal characteristics such as voice onset, terminations, and breaks [12]. This oversimplification is directly linked to a reduced discriminatory capability: according to the comparison between phonatory and articulation approaches described in [12], the use of articulation features together with ML techniques maximizes the performance of PD automatic detection models, with accuracy ranging from 80% to 95%.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, a detailed articulation analysis can investigate more specific aspects, less prone to variations due to the patient's emotional state. In more detail, features can be extracted from different types of sound regions and can be related to the speed or acceleration of articulation elements [12]. TRs between segments can be employed to describe the patient's ability to initiate and stop movements; the impaired articulation of different phonemes can be measured to investigate how the disease affects the regions of the phonation apparatus.…”

Section: Introductionmentioning

confidence: 99%

“…However, the aspects that contribute to the strong discriminatory potential of articulation analysis also lead to high complexity. While the analysis of consonants contains more information than vowels, the pronunciation of such phonemes varies depending on the language being considered [12], [14]. In addition, a detailed analysis of phonemes requires the use of more precise measurements than a prosody study.…”

Section: Introductionmentioning

confidence: 99%

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Speech Impairment in Parkinson’s Disease: Acoustic Analysis of Unvoiced Consonants in Italian Native Speakers

Amato¹,

Borzì

Olmo

et al. 2021

IEEE Access

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The study of the influence of Parkinson's Disease (PD) on vocal signals has received much attention over the last decades. Increasing interest has been devoted to articulation and acoustic characterization of different phonemes.Method: In this study we propose the analysis of the Transition Regions (TR) of specific phonetic groups to model the loss of motor control and the difficulty to start/stop movements, typical of PD patients. For this purpose, we extracted 60 features from pre-processed vocal signals and used them as input to several machine learning models. We employed two data sets, containing samples from Italian native speakers, for training and testing. The first dataset -28 PD patients and 22 Healthy Control (HC) -included recordings in optimal conditions, while in the second one -26 PD patients and 18 HC-signals were collected at home, using non-professional microphones. Results: We optimized two support vector machine models for the application in controlled noise conditions and home environments, achieving 98% ± 1.1 and 88% ± 2.8 accuracy in 10-fold cross-validation, respectively. Conclusion: This study confirms the high capability of the TRs to discriminate between PD patients and healthy controls, and the feasibility of automatic PD assessment using voice recordings. Moreover, the promising performance of the implemented model discloses the option of voice processing using low-cost devices and domestic recordings, possibly self-managed by the patients themselves.

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Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects

Cited by 101 publications

References 135 publications

Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions

Speech Impairment in Parkinson’s Disease: Acoustic Analysis of Unvoiced Consonants in Italian Native Speakers

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