Evaluation of the Neurological State of People with Parkinson’s Disease Using i-Vectors

Garcia, N.; Orozco-Arroyave, Juan Rafael; D’Haro, Luis Fernando; Dehak, Najim; Nöth, Elmar

doi:10.21437/interspeech.2017-819

Cited by 17 publications

(11 citation statements)

References 16 publications

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“…Speaker model based on i-vectors (Dehak et al, 2011a), the spoken language (Dehak et al, 2011b), gender, age, and others. We hypothesize that i-vectors may contain also information related to speech impairments like those developed by PD patients (Garcia et al, 2017).…”

Section: Intelligibilitymentioning

confidence: 99%

Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease

Vásquez-Correa

Orozco-Arroyave

Bocklet

et al. 2018

Journal of Communication Disorders

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The m-FDA scale was introduced to assess the dysarthria level of patients with PD. Articulation features extracted from continuous speech signals to create i-vectors were the most accurate to quantify the dysarthria level, with correlations of up to 0.69 between the predicted m-FDA scores and those assigned by the phoniatricians. When the dysarthria levels were estimated considering dedicated speech exercises such as rapid repetition of syllables (DDKs) and read texts, the correlations were 0.64 and 0.57, respectively. In addition, the combination of several feature sets and speech tasks improved the results, which validates the hypothesis about the contribution of information from different tasks and feature sets when assessing dysarthric speech signals. The speaker models seem to be promising to perform individual modeling for monitoring the dysarthria level of PD patients. The proposed approach may help clinicians to make more accurate and timely decisions about the evaluation and therapy associated to the dysarthria level of patients. The proposed approach is a great step towards unobtrusive/ecological evaluations of patients with dysarthric speech without the need of attending medical appointments.

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Section: Intelligibilitymentioning

confidence: 99%

Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease

Vásquez-Correa

Orozco-Arroyave

Bocklet

et al. 2018

Journal of Communication Disorders

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“…In [13], i-vectors were used for the representation of word segments produced by 15 dysarthric speakers resulting in some important correlations between automatically predicted and reference intelligibility measures. Finally, in [14], the authors proposed an approach based on a cosine distance between the i-vector representation of a speech production (test) and two reference i-vectors representing each normal and dysarthric speech.…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of Speech Intelligibility Based on I-vectors in the Context of Head and Neck Cancers

Laaridh¹,

Fredouille²,

Ghio³

et al. 2018

Interspeech 2018

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In disordered speech context, and despite its well-known subjectivity, perceptual evaluation is still the most commonly used method in clinical practice to evaluate the intelligibility level of patients' speech productions. However, and thanks to increasing computing power, automatic speech processing systems have witnessed a democratization in terms of users and application areas including the medical practice. In this paper, we evaluate an automatic approach for the prediction of cancer patients' speech intelligibility based on the representation of the speech acoustics in the total variability subspace based on the i-vector paradigm. Experimental evaluations of the proposed predictive approach have shown a very high correlation rate with perceptual intelligibility when applied on the French speech corpora C2SI (r=0.84). They have also demonstrated the robustness of the approach when using a limited amount of disordered speech per patient, which may lead to the redesign and alleviation of the test protocols usually used in disordered speech evaluation context.

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“…Following the study of [21], we performed our experiments using four different feature sets. The first consisted of 20 MFCCs, obtained from 30 ms wide windows; and the rest of the feature sets were built by articulation, phonation, and prosody, respectively.…”

Section: Feature Extractionmentioning

confidence: 99%

Assessing Parkinson’s Disease from Speech Using Fisher Vectors

2019

Self Cite

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Parkinson's Disease (PD) is a neuro-degenerative disorder that affects primarily the motor system of the body. Besides other functions, the subject's speech also deteriorates during the disease, which allows for a non-invasive way of automatic screening. In this study, we represent the utterances of subjects having PD and those of healthy controls by means of the Fisher Vector approach. This technique is very common in the area of image recognition, where it provides a representation of the local image descriptors via frequency and high order statistics. In the present work, we used four frame-level feature sets as the input of the FV method, and applied (linear) Support Vector Machines (SVM) for classifying the speech of subjects. We found that our approach offers superior performance compared to classification based on the i-vector and cosine distance approach, and it also provides an efficient combination of machine learning models trained on different feature sets or on different speaker tasks.

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Evaluation of the Neurological State of People with Parkinson’s Disease Using i-Vectors

Cited by 17 publications

References 16 publications

Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease

Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease

Automatic Evaluation of Speech Intelligibility Based on I-vectors in the Context of Head and Neck Cancers

Assessing Parkinson’s Disease from Speech Using Fisher Vectors

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