Discriminating Parkinson and Healthy People Using Phonation and Cepstral Features of Speech

Upadhya, Savitha S.; Cheeran, Alice N.

doi:10.1016/j.procs.2018.10.376

Cited by 23 publications

(10 citation statements)

References 9 publications

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“…During this step, each of the frames of N values is converted from the temporal domain to the frequency domain. The FFT is a fast algorithm for the calculation of the Discrete Fourier Transform (DFT) is defined by the formula (6). The values obtained are called the spectrum.…”

Section: The Fast Fourier Transform (Fft)mentioning

confidence: 99%

“…Diagnosis of PD from voice signals has been studied by many researchers [2,5]; the most widely explored characteristics to detect PD are phonation features, which include jitter variants, shimmer variants, noise variants [6,8]. The Mel Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), Linear Predictive Coefficients (LPC), and Linear Predictive Cepstral Coefficients (LPCC) are some of the other features studied by researchers [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Speech analysis for the detection of Parkinson’s disease by combined use of empirical mode decomposition, Mel frequency cepstral coefficients, and the K-nearest neighbor classifier

et al. 2022

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Parkinson’s disease (PD) is one of the neurodegenerative diseases. The neuronal loss caused by this disease leads to symptoms such as lack of initiative, depressive states, psychological disorders, and impairment of cognitive functions as well as voice dysfunctions. This paper aims to propose a system of automatic recognition of Parkinson’s disease by voice analysis. In this system, we are based on a database of 38 recordings, 20 people with Parkinson’s disease and 18 healthy people pronounce the vowel /a/.at first, we have decomposed the vocal signal of each patient by the Empirical Mode Decomposition (EMD), then, we extract from 1 to 12 coefficients of the Mel Frequency Cepstral Coefficients (MFCC), to obtain the voiceprint from each voice sample, we compressed the frames by computing their average value. At the end of the classification, we have used the validation scheme “holdout” as well as the K-nearest neighbor (KNN) classifier, the performance of this classification gives accuracy up to 86,67% when applied to 80% of the database as training data.

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Section: The Fast Fourier Transform (Fft)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speech analysis for the detection of Parkinson’s disease by combined use of empirical mode decomposition, Mel frequency cepstral coefficients, and the K-nearest neighbor classifier

et al. 2022

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“…A new system is proposed in [31] to analyze and differentiate between normal and pathological voices with respect to Parkinson's Disease (PD). The voice signals are extracted using two speech features which are phonation and cepstral features.…”

Section: Related Workmentioning

confidence: 99%

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

et al. 2021

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In the last decade, the implementation of machine learning algorithms in the analysis of voice disorder is paramount in order to provide a non-invasive voice pathology detection by only using audio signal. In spite of that, most recent systems of voice pathology work on a limited acoustic database. In other words, the systems use one vowel, such as /a/, and ignore sentences and other vowels when analyzing the audio signal. Other key issues that should be considered in the systems are accuracy and time consumption of an algorithm. Online Sequential Extreme Learning Machine (OSELM) is one of the machine learning algorithms that can be regarded as a rapid and accurate algorithm in the classification process. Therefore, this paper presents a voice pathology detection and classification system by using OSELM algorithm as a classifier, and Mel-frequency cepstral coefficient (MFCC) as a featured extraction. In this work, the voice samples were taken from the Saarbrücken voice database (SVD). This system involves two parts of the database; the first part includes all voices in SVD with sentences and vowels /a/, /i/, and /u/, which are uttered in high, low, and normal pitches; and the second part utilizes voice samples of the common three types of pathologies (cyst, polyp, and paralysis) based on the vowel /a/ that is produced in normal pitch. The experimental results have shown that OSELM was able to achieve the highest accuracy up to 91.17%, 94% of precision, and 91% of recall. Furthermore, OSELM obtained 87%, 87.55%, and 97.67% for f-measure, G-mean, and specificity, respectively. The proposed system also presents a high ability to achieve detection and classification results in real-time clinical applications.

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“…Data definition, understanding and modelling for PD detection is explained in [13]. Mel frequency cepstral coefficients (MFCC) [4,7,9,11] Joint time-frequency analysis (JTFA) [10] Praat and multidimensional voice program (MDVP) [14] Tunable Q-factor wavelet transform (TQWT) [15] Intrinsic mode function cepstral coefficient (IMFCC) [16] 3 Signal Processing Methods…”

Section: Databasesmentioning

confidence: 99%

“…The patient's Spontaneous Speech (SS) and extracted features might be used for the early detection and progression of the diseases like Alzheimer's, Parkinson's, Huntington's and Autism. Due to PD, the muscles used in the speech production are affected and thereby altering the various features of speech [4]. So, SS and extracted features may be treated as sophisticated biomarker for identification of disease and its progression [5].…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Parkinson’s Disease Through Speech Features and Machine Learning: A Review

Gullapalli

Mittal

2021

Smart Innovation, Systems and Technologies

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Parkinson's Disease (PD) is a kind of neurodegenerative disorder. The disease causes communication impairment based on its progression. In general, identification of PD carried out based on medical images of brain. But it was recently identified that voice is acting as biomarkers for several neurological disorders. A review of speech features and machine learning algorithms is presented. This might be helpful for development of a non-invasive signal processing techniques for early detection of PD. Several models developed for disease detection is discussed, which are developed based on features like acoustic, phonation, articulation, dysphonia, etc. Machine learning algorithms like Logistic Regression (LG), Support Vector Machine (SVM), Boosting Regression Tree, bagging Regression, etc., and their performance accuracies in classification of Patient with PD (PWP) and Healthy Controls (HC) are reviewed. All these classification algorithms are trained and tested on several repository corpuses and customized datasets. The Spontaneous Speech (SS) is an efficient tool for the early detection of diseases like Parkinson's, Alzheimer's, Autism and several other dementia types in elderly people.

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Discriminating Parkinson and Healthy People Using Phonation and Cepstral Features of Speech

Cited by 23 publications

References 9 publications

Speech analysis for the detection of Parkinson’s disease by combined use of empirical mode decomposition, Mel frequency cepstral coefficients, and the K-nearest neighbor classifier

Speech analysis for the detection of Parkinson’s disease by combined use of empirical mode decomposition, Mel frequency cepstral coefficients, and the K-nearest neighbor classifier

Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

Early Detection of Parkinson’s Disease Through Speech Features and Machine Learning: A Review

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