Parkinson’s Disease and Aging: Analysis of Their Effect in Phonation and Articulation of Speech (original) (raw)
Related papers
IEEE Access
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.
Phonation and Articulation Analysis of Spanish Vowels for Automatic Detection of Parkinson’s Disease
Lecture Notes in Computer Science, 2014
Parkinson's disease (PD) is a chronic neurodegenerative disorder of the nervous central system and it can affect the communication skills of the patients. There is an interest in the research community to develop computer aided tools for the analysis of the speech of people with PD for detection and monitoring. In this paper, three new acoustic measures for the simultaneous analysis of the phonation and articulation of patients with PD are presented. These new measures along with other classical articulation and perturbation features are objectively evaluated with a discriminant criterion. According to the results, the speech of people with PD can be detected with an accuracy of 81% when phonation and articulation features are combined.
Acoustic analysis of voice and speech characteristics in early untreated Parkinson's disease
parkinson's disease (pd) is a neurological illness characterized by progressive lost of dopaminergic neurons, primarily in the substantia nigra pars compacta. changes in speech associated with hypokinetic dysarthria are a common manifestation in patients with idiopathic pd. the aim of this study is to investigate the feasibility of automated acoustic measures for the identification of voice and speech disorders in pd. the speech data were collected from 46 czech native speakers, 24 with early pd before receiving pharmacotherapy treatment. We have applied several traditional and non-standard measurements in combination with statistical decision-making strategy to assess the extent of vocal impairment of recruited speakers. subsequently, we have applied support vector machine to find the best combination of measurements to differentiate pd from healthy subjects. this method leads to overall classification performance of 85%. admittedly, we have found relationships between measures of phonation and articulation and bradykinesia and rigidity in pd. in conclusion, the acoustic analysis can ease the clinical assessment of voice and speech disorders, and serve as measures of clinical progression as well as in the monitoring of treatment effects.
Detection of Persons with Parkinson’s Disease by Acoustic, Vocal, and Prosodic Analysis
70 % to 90 % of patients with Parkinson's disease (PD) show an affected voice. Various studies revealed, that voice and prosody is one of the earliest indicators of PD. The issue of this study is to automatically detect whether the speech/voice of a person is affected by PD. We employ acoustic features, prosodic features and features derived from a two-mass model of the vocal folds on different kinds of speech tests: sustained phonations, syllable repetitions, read texts and monologues. Classification is performed in either case by SVMs. A correlation-based feature selection was performed, in order to identify the most important features for each of these systems. We report recognition results of 91 % when trying to differentiate between normal speaking persons and speakers with PD in early stages with prosodic modeling. With acoustic modeling we achieved a recognition rate of 88 % and with vocal modeling we achieved 79 %. After feature selection these results could greatly be improved. But we expect those results to be too optimistic. We show that read texts and monologues are the most meaningful texts when it comes to the automatic detection of PD based on articulation, voice, and prosodic evaluations. The most important prosodic features were based on energy, pauses and F0. The masses and the compliances of spring were found to be the most important parameters of the two-mass vocal fold model. 978-1-4673-0367-5/11/$26.00
Automatic evaluation of parkinson's speech — acoustic, prosodic and voice related cues
Interspeech 2013, 2013
Articulation and phonation is affected in 70 % to 90 % of patients with Parkinson's disease (PD). This study focuses on the question whether speech carries information about 1. PD being present at a speaker or not, and 2. estimating the severity of PD (if present). We first perform classification experiments focusing on the automatic detection of PD as a 2-class problem (PD vs. healthy speakers). The detection of severity is described as a 3-class task based on the Unified Parkinson's Disease Rating Scale (UPDRS) ratings. We employ acoustic, prosodic and glottal features on different kinds of speech tests: various syllable repetition tasks, read sentences and texts, and monologues. Classification is performed in either case by SVMs. We report recognition results of 81.9 % when trying to differentiate between normally speaking persons and speakers with PD. With system fusion we achieved a recognition results of 59.1 % on the task of UPDRS classification.
New Spanish speech corpus database for the analysis of people suffering from Parkinson’s disease
LREC2014
Parkinsons disease (PD) is the second most prevalent neurodegenerative disorder after Alzheimer's, affecting about 1% of the people older than 65 and about 89% of the people with PD develop different speech disorders. Different researchers are currently working in the analysis of speech of people with PD, including the study of different dimensions in speech such as phonation, articulation, prosody and intelligibility. The study of phonation and articulation has been addressed mainly considering sustained vowels; however, the analysis of prosody and intelligibility requires the inclusion of words, sentences and monologue. In this paper we present a new database with speech recordings of 50 patients with PD and their respective healthy controls, matched by age and gender. All of the participants are Spanish native speakers and the recordings were collected following a protocol that considers both technical requirements and several recommendations given by experts in linguistics, phoniatry and neurology. This corpus includes tasks such as sustained phonations of the vowels, diadochokinetic evaluation, 45 words, 10 sentences, a reading text and a monologue. The paper also includes results of the characterization of the Spanish vowels considering different measures used in other works to characterize different speech impairments.
Perceptual Analysis of Speech Signals from People with Parkinson’s Disease
Lecture Notes in Computer Science, 2013
Parkinson's disease (PD) is a neurodegenerative disorder of the nervous central system and it affects the limbs motor control and the communication skills of the patients. The evolution of the disease can get to the point of affecting the intelligibility of the patient's speech. The treatments of the PD are mainly focused on improving limb symptoms and their impact on speech production is still unclear. Considering the impact of the PD in the intelligibility of the patients, this paper explores the discrimination capability of different perceptual features in the task of automatic classification of speech signals from people with Parkinson's disease (PPD) and healthy controls (HC). The experiments presented in this paper are performed considering the five Spanish vowels uttered by 20 PPD and 20 HC. The considered set of features includes linear prediction coefficients (LPC), linear prediction cepstral Coefficients (LPCC), Mel-frequency cepstral coefficients (MFCC), perceptual linear prediction coefficients (PLP) and two versions of the relative spectra coefficients (RASTA). Accordin the results for vowels /e/ and /o/ it is not enough to consider one kind of perceptual features, it is required to perform combination of different coefficients such as PLP, MFCC and RASTA. For the case of the remaining vowels, the best results are obtained considering only one kind of perceptual features, PLP for vowel /a/ and MFCC for vowels /i/ and /u/.
Robust and language-independent acoustic features in Parkinson's disease
Frontiers in Neurology, 2023
The analysis of vocal samples from patients with Parkinson's disease (PDP) can be relevant in supporting early diagnosis and disease monitoring. Intriguingly, speech analysis embeds several complexities influenced by speaker characteristics (e.g., gender and language) and recording conditions (e.g., professional microphones or smartphones, supervised, or non-supervised data collection). Moreover, the set of vocal tasks performed, such as sustained phonation, reading text, or monologue, strongly a ects the speech dimension investigated, the feature extracted, and, as a consequence, the performance of the overall algorithm. Methods: We employed six datasets, including a cohort of Healthy Control (HC) participants and PDP from di erent nationalities (i.e., Italian, Spanish, Czech), recorded in variable scenarios through various devices (i.e., professional microphones and smartphones), and performing several speech exercises (i.e., vowel phonation, sentence repetition). Aiming to identify the e ectiveness of di erent vocal tasks and the trustworthiness of features independent of external co-factors such as language, gender, and data collection modality, we performed several intra-and inter-corpora statistical analyses. In addition, we compared the performance of di erent feature selection and classification models to evaluate the most robust and performing pipeline. Results: According to our results, the combined use of sustained phonation and sentence repetition should be preferred over a single exercise. As for the set of features, the Mel Frequency Cepstral Coe cients demonstrated to be among the most e ective parameters in discriminating between HC and PDP, also in the presence of heterogeneous languages and acquisition techniques. Conclusion: Even though preliminary, the results of this work can be exploited to define a speech protocol that can e ectively capture vocal alterations while minimizing the e ort required to the patient. Moreover, the statistical analysis identified a set of features minimally dependent on gender, language, and recording modalities. This discloses the feasibility of extensive cross-corpora tests to develop robust and reliable tools for disease monitoring and staging and PDP follow-up.
IEEE/ACM Transactions on Audio, Speech, and Language Processing
Speech from people with Parkinson's disease (PD) are likely to be degraded on phonation, articulation, and prosody. Motivated to describe articulation deficits comprehensively, we investigated 1) the universal phonological features that model articulation manner and place, also known as speech attributes, and 2) glottal features capturing phonation characteristics. These were further supplemented by, and compared with, prosodic features using a popular compact feature set and standard MFCC. Temporal characteristics of these features were modeled by convolutional neural networks. Besides the features, we were also interested in the speech tasks for collecting data for automatic PD speech assessment, like sustained vowels, text reading, and spontaneous monologue. For this, we utilized a recently collected Finnish PD corpus (PDSTU) as well as a Spanish database (PC-GITA). The experiments were formulated as regression problems against expert ratings of PD-related symptoms, including ratings of speech intelligibility, voice impairment, overall severity of communication disorder on PDSTU, as well as on the Unified Parkinson's Disease Rating Scale (UPDRS) on PC-GITA. The experimental results show: 1) the speech attribute features can well indicate the severity of pathologies in parkinsonian speech; 2) combining phonation features with articulatory features improves the PD assessment performance, but requires high-quality recordings to be applicable; 3) read speech leads to more accurate automatic ratings than the use of sustained vowels, but not if the amount of speech is limited to correspond to the sustained vowels in duration; and 4) jointly using data from several speech tasks can further improve the automatic PD assessment performance.