Phonological i-Vectors to Detect Parkinson’s Disease
Nicanor Garcia-Ospina,Tomas Arias-Vergara,Juan Camilo Vásquez-Correa,Juan Rafael Orozco-Arroyave,Milos Cernak,Elmar Nöth +5 more
- pp 462-470
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TLDR
A recently developed method to extract phonological features from speech signals based on the Sound Patterns of English phonological model is used, which could be helpful to assess new specific speech aspects such as the movement of different articulators involved in the speech production process.Abstract:
Speech disorders are common symptoms among Parkinson’s disease patients and affect the speech of patients in different aspects Currently, there are few studies that consider the phonological dimension of Parkinson’s speech In this work, we use a recently developed method to extract phonological features from speech signals These features are based on the Sound Patterns of English phonological model The extraction is performed using pre-trained Deep Neural Networks to infer the probabilities of phonological features from short-time acoustic features An i-vector extractor is trained with the phonological features The extracted i-vectors are used to classify patients and healthy speakers and assess their neurological state and dysarthria level This approach could be helpful to assess new specific speech aspects such as the movement of different articulators involved in the speech production processread more
Citations
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Journal ArticleDOI
Multi-channel spectrograms for speech processing applications using deep learning methods
Tomas Arias-Vergara,Tomas Arias-Vergara,Tomas Arias-Vergara,Philipp Klumpp,Juan Camilo Vásquez-Correa,Juan Camilo Vásquez-Correa,Elmar Nöth,Juan Rafael Orozco-Arroyave,Juan Rafael Orozco-Arroyave,Maria Schuster +9 more
TL;DR: This paper proposes a methodology to combine three different time–frequency representations of the signals by computing continuous wavelet transform, Mel-spectrograms, and Gammatone spectrograms and combining then into 3D-channel spectrogram to analyze speech in two different applications: automatic detection of speech deficits in cochlear implant users and phoneme class recognition to extract phone-attribute features.
Journal ArticleDOI
X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech.
Laetitia Jeancolas,Dijana Petrovska-Delacrétaz,Graziella Mangone,Badr-Eddine Benkelfat,Jean-Christophe Corvol,Marie Vidailhet,Stéphane Lehéricy,Stéphane Lehéricy,Habib Benali +8 more
TL;DR: In this paper, a deep neural network (DNN) was used to detect Parkinson's disease (PD) at an early stage using voice analysis in French speakers with a high-quality microphone and via the telephone network.
Journal ArticleDOI
X-vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection from Speech
Laetitia Jeancolas,Dijana Petrovska-Delacrétaz,Graziella Mangone,Badr-Eddine Benkelfat,Jean-Christophe Corvol,Marie Vidailhet,Stéphane Lehéricy,Stéphane Lehéricy,Habib Benali +8 more
TL;DR: X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7–15% improvement).
References
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Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results.
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TL;DR: The combined clinimetric results of this study support the validity of the MDS‐UPDRS for rating PD.
Journal ArticleDOI
Front-End Factor Analysis for Speaker Verification
TL;DR: An extension of the previous work which proposes a new speaker representation for speaker verification, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis, named the total variability space because it models both speaker and channel variabilities.
Journal ArticleDOI
Differential Diagnostic Patterns of Dysarthria
TL;DR: Thirty-second speech samples were studied of at least 30 patients in each of 7 discrete neurologic groups, each patient unequivocally diagnosed as being a representative of his diagnostic group, leading to results leading to these conclusions.
Proceedings Article
Analysis of i-vector Length Normalization in Speaker Recognition Systems.
TL;DR: The proposed approach deals with the nonGaussian behavior of i-vectors by performing a simple length normalization, which allows the use of probabilistic models with Gaussian assumptions that yield equivalent performance to that of more complicated systems based on Heavy-Tailed assumptions.
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The clinical symptoms of Parkinson's disease.
TL;DR: In this review, the clinical features of Parkinson's disease, both motor and non‐motor, are described in the context of the progression of the disease.
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