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1

An investigation about the relationship between dysarthria level of speech and the neurological state of Parkinson’s patients

Karan Biswajit, Sekhar Sahu Sitanshu, Orozco-Arroyave Juan Rafael

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

710--726

EN

Parkinson’s disease (PD) is the most common neurological disorder that typically affects elderly people. In the earlier stage of disease, it has been seen that 90% of the patients develop voice disorders namely hypokinetic dysarthria. As time passes, the severity of PD increases, and patients have difficulty performing different speech tasks. During the progression of the disease, due to less control of articulatory organs such as the tongue, jaw, and lips, the quality of speech signals deteriorates. Periodic medical evaluations are very important for PD patients; however, having access to a medical appointment with a neurologist is a privilege in most countries. Considering that the speech recording process is inexpensive and very easy to do, we want to explore in this paper the suitability of mapping information of the dysarthria level into the neurological state of patients and vice versa. Three levels of severity are considered in a multiclass framework using time-frequency (TF) features and random-forest along with an Error-Correcting Output Code (ECOC) approach. The multiclass classification task based on dysarthria level is performed using the TF features with words and diadochokinetic (DDK) speech tasks. The developed model shows an unweighted average recall (UAR) of 68.49% with the DDK task /pakata/ based on m-FDA level, and 48.8% with the word /petaka/ based on the UPDRS level using the Random Forest classifier. With the aim, to evaluate the neurological states using the dysarthria level, the developed models are used to predict the MDS-UPDRS-III level of patients. The highest matching accuracy of 32% with the word /petaka/ is achieved. Similarly, the multiclass classification framework based on MDS-UPDRS-III is applied to predict the dysarthria level of patients. In this case, the highest matching accuracy of 18% was obtained with the DDK tasks /pataka/.

2

An improved framework for Parkinson’s disease prediction using Variational Mode Decomposition-Hilbert spectrum of speech signal

Karan Biswajit, Sekhar Sahu Sitanshu

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

717--732

EN

Parkinson’s disease (PD) is a neuro-degenerative disease due to loss of brain cells, which produces dopamine. It is most common after Alzheimer’s disease specially seen in old age people. In the earlier stage of disease, it has been noticed that most of the people suffering from speech disorder. From last two decades many studies have been conducted for the analysis of vocal tremors in PD. This study explores the combined approach of Variational Mode Decomposition (VMD) and Hilbert spectrum analysis (HSA) to investigate the voice tremor of patients with PD. A new set of features Hilbert cepstral coefficients (HCCs) are proposed in this study. Proposed features are assessed using vowels and words of PC-GITA database. The effectiveness of HCC features is utilized to perform classification, and regression analysis for PD detection. The highest average classification accuracy up to 91% and 96% is obtained with vowel /a/ and word /apto/ respectively. Further the classification accuracy up to 82% is obtained with independent dataset, when tested with the optimized model developed using PC-GITA database. In dysarthria level prediction highest correlation up to 0.82 is obtained using vowel /a/ and 0.8 with word /petaka/. The outcomes of this study indicate that the proposed articulatory features are suitable and accurate for PD assessment.

3

A speech recognition system based on electromyography for the rehabilitation of dysarthric patients: A Thai syllable study

Sae Jong Nida, Phukpattaranont Pornchai

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

234--245

EN

The objective of this study is to develop a speech recognition system for classifying nine Thai syllables, which is used for the rehabilitation of dysarthric patients, based on five channels of surface electromyography (sEMG) signals from the human articulatory muscles. After the sEMG signal from each channel was collected, it was processed by a band-pass filter from 20–450 Hz for noise removal. Then, six features from three feature categories were determined and analyzed, namely, mean absolute value (MAV) and wavelength (WL) from amplitude based features (ABF), zero crossing (ZC) and mean frequency (MNF) from frequency based features (FBF), and L-kurtosis (L-KURT) and L-skewness (L-SKW) from statistics based features (SBF). Subsequently, a spectral regression extreme learning machine (SRELM) was used as the feature projection technique to reduce the dimension of feature vector from 30 to 8. Finally, the projected features were classified using a feed forward neural network (NN) classifier with 5-fold cross-validation. The proposed system was evaluated with the sEMG signals from seven healthy volunteers and five dysarthric volunteers. The results show that the proposed system can recognize the sEMG signals from both healthy and dysarthric volunteers. The average classification accuracies obtained from all six features in the healthy and dysarthric volunteers were 94.5% and 89.4%, respectively.

4

Fully automated speaker identification and intelligibility assessment in dysarthria disease using auditory knowledge

Kadi K. L., Selouani S. A., Boudraa B., Boudraa M.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

233--247

EN

Millions of children and adults suffer from acquired or congenital neuro-motor communication disorders that can affect their speech intelligibility. The automatically characterization of speech impairment can contribute to improve the patient's life quality, and assist experts in assessment and treatment design. In this paper, we present new approaches to improve the analysis and classification of disordered speech. First, we propose an automatic speaker recognition approach especially adapted to identify dysarthric speakers. Secondly, we suggest a method for the automatic assessment of the dysarthria severity level. For this purpose, a model simulating the external, middle and inner parts of the ear is presented. This ear model provides relevant auditory-based cues that are combined with the usual Mel-Frequency Cepstral Coefficients (MFCC) to represent atypical speech utterances. The experiments are carried out by using data of both Nemours and Torgo databases of dysarthric speech. Gaussian Mixture Models (GMMs), Support Vector Machines (SVMs) and hybrid GMM/SVM systems are tested and compared in the context of dysarthric speaker identification and assessment. The experimental results achieve a correct speaker identification rate of 97.2% which can be considered promising for this novel approach; also the existing assessment systems are outperformed with a 93.2% correct classification rate of dysarthria severity levels.

5

Classification of speech intelligibility in Parkinson's disease

Khan T., Westin J., Dougherty M.

Biocybernetics and Biomedical Engineering

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2014

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Vol. 34, no. 1

35--45

EN

A problem in the clinical assessment of running speech in Parkinson's disease (PD) is to track underlying deficits in a number of speech components including respiration, phonation, articulation and prosody, each of which disturbs the speech intelligibility. A set of 13 features, including the cepstral separation difference and Mel-frequency cepstral coefficients were computed to represent deficits in each individual speech component. These features were then used in training a support vector machine (SVM) using n-fold cross validation. The dataset used for method development and evaluation consisted of 240 running speech samples recorded from 60 PD patients and 20 healthy controls. These speech samples were clinically rated using the Unified Parkinson's Disease Rating Scale Motor Examination of Speech (UPDRS-S). The classification accuracy of SVM was 85% in 3 levels of UPDRS-S scale and 92% in 2 levels with the average area under the ROC (receiver operating characteristic) curves of around 91%. The strong classification ability of selected features and the SVM model supports suitability of this scheme to monitor speech symptoms in PD.

6

Cepstral separation difference: A novel approach for speech impairment quantification in Parkinson's disease

Khan T., Westin J., Dougherty M.

Biocybernetics and Biomedical Engineering

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2014

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Vol. 34, no. 1

25--34

EN

This paper introduces a novel approach, Cepstral Separation Difference (CSD), for quantification of speech impairment in Parkinson's disease (PD). CSD represents a ratio between the magnitudes of glottal (source) and supra-glottal (filter) log-spectrums acquired using the source-filter speech model. The CSD-based features were tested on a database consisting of 240 clinically rated running speech samples acquired from 60 PD patients and 20 healthy controls. The Guttmann (μ2) monotonic correlations between the CSD features and the speech symptom severity ratings were strong (up to 0.78). This correlation increased with the increasing textual difficulty in different speech tests. CSD was compared with some non-CSD speech features (harmonic ratio, harmonic-to-noise ratio and Mel-frequency cepstral coefficients) for speech symptom characterization in terms of consistency and reproducibility. The high intra-class correlation coefficient (>0.9) and analysis of variance indicates that CSD features can be used reliably to distinguish between severity levels of speech impairment. Results motivate the use of CSD in monitoring speech symptoms in PD.

7

Endogenne potencjały wywołane u osób z dysartrycznymi zaburzeniami mowy w naczyniopochodnym uszkodzeniu mózgu

Słotwiński K., Budrewicz S., Koszewicz M., Podemski R.

Acta Bio-Optica et Informatica Medica. Inżynieria Biomedyczna

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2010

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Vol. 16, nr 2

179-181

PL

Celem pracy jest elektrofizjologiczna ocena endogennych potencjałów wywołanych (słuchowy potencjał P300) u pacjentów z dysartrycznymi zaburzeniami mowy i naczyniopochodnym uszkodzeniem mózgu. Badaniem objęto grupę 30 pacjentów z niedokrwiennym udarem mózgu, potwierdzonym tomografią komputerową (TK) oraz 25-osobową odpowiednio dobraną wiekowo grupę kontrolną. U pacjentów po przebytym naczyniopochodnym uszkodzeniu mózgu zanotowano znacznie wydłużony czas latencji fali potencjału P300 oraz obniżenie jego amplitudy, bez istotnych korelacji pomiędzy parametrami P300 a stopniem nasilenia dysartrii.

EN

The purpose of the study was electrophysiological assessment of endogenous evoked potentials in patients with dysarthria after ischaemic cerebral damage, based on event-related auditory P300 potential. The study comprised 30 patients with ischaemic cerebral damage confirmed by CT (computer tomography) head scan and 25 age-matched control group. In patients with dysarthria after ischaemic cerebral damage significantly prolonged P300 latency and decrease in amplitude was found, with no significant correlation between P300 parameters and with the severity of dysarthria.

8

Automatyczna ocena zaburzeń emisji głosu będących wynikiem procesów neurodegeneracyjnych w oparciu o analizę wyizolowanych głosek

Orzechowski T., Chmurzyńska K., Radkowski P.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

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2006

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T. 10, z. 3

91-97

PL

Prezentowane wyniki stanowią początek badań nad automatyczną klasyfikacją głosu. W niniejszej pracy zarysowano teoretyczne podstawy fizjologiczne głosu, patologiczne zmiany w mowie powodowane dyzartrią, następnie scharakteryzowano dobór materiału lingwistycznego pod względem miejsca i sposobu artykulacji w systemie fonetycznym języka polskiego. Kolejne miejsce w pracy zajmuje opis rejestracji i wstępnej analizy głosu badanych (zmiany w realizacji głosek, natężenie głosek wymawianych wielokrotnie w izolacji, analiza widma dźwięków ciągłych). Zjawiska słyszane w badaniu subiektywnym patologa mowy, bądź neurologa zostały potwierdzone precyzyjnym badaniem obiektywnym. Uzyskane parametry pozwalają na sparametryzowanie wyników badań, umożliwiające kompleksową klasyfikację. Pozwoli to również na dokładną ocenę progresji choroby, niemożliwą w klasycznym badaniu subiektywnym.

EN

This paper presents results of preliminary research of voice pathological changes caused by dysarthria. Computer analysis of voice may lead to identification of parameters correlated with neurological diseases. The selection of linguistic material was characterized according to the place and manner of articulation in the phonetic system of Polish. Results of clinical examination allowed to determine simple markers of neurodegenerative diseases, which will serve as a basis for construction of objective examination model.