Perceptual Analysis of Speech Signals from People with Parkinson’s Disease

Orozco-Arroyave, Juan Rafael; Arias-Londoño, Julián D.; Vargas-Bonilla, Jesús Francisco; Nöth, Elmar

doi:10.1007/978-3-642-38637-4_21

Cited by 34 publications

(43 citation statements)

References 18 publications

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“…Speech signals are analyzed based on the automatic detection of onset and offset transitions, which model the difficulties of the patients to start/stop the movement of the vocal folds. The detection of the transitions is based on the presence of the fundamental frequency of speech in short-time frames, as it was shown in [8]. The border between voiced and unvoiced frames is detected, and 80 ms of the signal are taken to the left and to the right, forming segments with 160 ms length.…”

Section: Segmentationmentioning

confidence: 99%

Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson’s Disease from Speech in Three Different Languages

Vásquez-Correa

Arias-Vergara

Ríos-Urrego

et al. 2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Parkinson's disease patients develop different speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows the development of computer aided tools to support the diagnosis and the evaluation of the disease severity. This paper introduces a methodology to classify Parkinson's disease from speech in three different languages: Spanish, German, and Czech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy among the three languages. The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set. The results suggest that the proposed strategy improves the accuracy of the models in up to 8% when the base model used to initialize the weights of the classifier is robust enough. In addition, the results obtained after the transfer learning are in most cases more balanced in terms of specificity-sensitivity than those trained without the transfer learning strategy.

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Section: Segmentationmentioning

confidence: 99%

Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson’s Disease from Speech in Three Different Languages

Vásquez-Correa

Arias-Vergara

Ríos-Urrego

et al. 2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Self Cite

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show abstract

“…the patients to start/stop the movement of the vocal folds. The method used to identify the transitions is based on the presence of the fundamental frequency of speech (pitch) in short-time frames as it was shown in [9]. The transition is detected, and 80 ms of the signal are taken to the left and to the right of each border, forming segments with 160 ms length ( Figure 2).…”

Section: Datamentioning

confidence: 99%

Multi-channel Convolutional Neural Networks for Automatic Detection of Speech Deficits in Cochlear Implant Users

Arias-Vergara

Vásquez-Correa

Gollwitzer

et al. 2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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This paper proposes a methodology for automatic detection of speech disorders in Cochlear Implant users by implementing a multi-channel Convolutional Neural Network. The model is fed with a 2-channel input which consists of two spectrograms computed from the speech signals using Mel-scaled and Gammatone filter banks. Speech recordings of 107 cochlear implant users (aged between 18 and 89 years old) and 94 healthy controls (aged between 20 and 64 years old) are considered for the tests. According to the results, using 2-channel spectrograms improves the performance of the classifier for automatic detection of speech impairments in Cochlear Implant users.

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“…Many researches have also been used on speech characteristics for the diagnosis of Parkinson's disease such as PLP, MFCC and Rasta-PLP [10][11][12][13][14]. Savitha S. Upadhyaa et al [10] worked on the detection of Parkinson's disease from the extraction of MFCCs using the multitaper Thomson windowing technique.…”

Section: Introductionmentioning

confidence: 99%

“…Savitha S. Upadhyaa et al [10] worked on the detection of Parkinson's disease from the extraction of MFCCs using the multitaper Thomson windowing technique. Orozco -Arroyave et al [11], obtained a 60% accuracy by extracting the MFCC coefficient as recognition accuracy. Achraf Benba et al [12] obtained a percentage accuracy (80%) by the combination of MFCC and the SVM [12], on a database of 17 healthy patients and 17 Parkinson patients.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

Drissi¹,

Soumaya²,

Nsiri³

et al. 2019

ijacsa

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The aim of this study presented in this paper is to determine the choice of the appropriate wavelet analyzer with the method of extraction of MFCC coefficients for an assistance in the diagnosis of Parkinson's disease. The analysis used is based on a database of 18 healthy and 20 Parkinsonian patients. The suggested processing is based on the transformation of the speech signal by the wavelet transform through testing several sorts of wavelets, extracting Mel Frequency Cepstral Coefficients (MFCC) from the signals, and we apply the support vector machine (SVM) as classifier. The test results reveal that the best recognition rate, which is 86.84%, is obtained by the wavelets of level 2 at 3 rd scale (Daubechie, Symlet, ReverseBior or BiorSpline) combination-MFCC-SVM.

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Perceptual Analysis of Speech Signals from People with Parkinson’s Disease

Cited by 34 publications

References 18 publications

Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson’s Disease from Speech in Three Different Languages

Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson’s Disease from Speech in Three Different Languages

Multi-channel Convolutional Neural Networks for Automatic Detection of Speech Deficits in Cochlear Implant Users

Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

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