Convolutional Neural Network to Model Articulation Impairments in Patients with Parkinson’s Disease

Text, Speech, and Dialogue

Strauss

et al. 2019

Parkinson's disease (PD) is a neuro-degenerative disorder that produces motor and non-motor impairments. Non-motor impairments include communication and mood disorders. Most of the studies in the literature have been focused on the analysis of motor symptoms. However, non-motor signs are also present in most of the cases. This paper addresses the study of language production as a potential tool to diagnose and monitor the neurological state of PD patients. The study proposes the use of natural language processing methods to extract features from transcriptions obtained from spontaneous speech recordings to discriminate between healthy control people and PD patients. The analysis considered classical features such as Bag of Words and Term Frequency-Inverse Document Frequency, along with methods based on word-embeddings. Accuracies of up to 72% are obtained when discriminating between PD patients and healthy subjects, which confirms that there is information embedded in the language production that can be used for the assessment of the disease.

Section: Resultssupporting

confidence: 90%

Natural Language Analysis to Detect Parkinson’s Disease

Pérez-Toro

Text, Speech, and Dialogue

Strauss

et al. 2019

“…The transition is detected, and 80 ms of the signal are taken to the left and to the right of each border, forming "chunks" of signals with 160 ms length. Each chunk is transformed into a time-frequency representation using the short-time Fourier transform (STFT) and used as input to a CNN [15]. The CNN extracts the most suitable features from the STFT and makes the final decision about whether the utterance corresponds to a PD patient or a HC speaker, or classify the speaker according to the level of the speech item in the third part of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III).…”

Section: Cnn Modelingmentioning

confidence: 99%

Unobtrusive Monitoring of Speech Impairments of Parkinson'S Disease Patients Through Mobile Devices

Arias-Vergara

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Orozco-Arroyave

et al. 2018

Parkinson's disease (PD) produces several speech impairments in the patients. Automatic classification of PD patients is performed considering speech recordings collected in noncontrolled acoustic conditions during normal phone calls in a unobtrusive way. A speech enhancement algorithm is applied to improve the quality of the signals. Two different classification approaches are considered: the classification of PD patients and healthy speakers and a multi-class experiment to classify patients in several stages of the disease. According to the results it is possible to classify PD patients and healthy controls with a AUC of up to 0.87. This work is a step forward to the development of telemonitoring systems to assess the speech of the patients.

“…Recently in [3] automatic classification using Support Vector Machines (SVM) between 20 CI users and 20 healthy speakers was performed in order to evaluate articulation disorders considering acoustic features. For the case of pathological speech detection, CNNs have outperformed classical machine learning methods [4][5][6]. In these studies, the conventional method is to perform time-frequency analysis by computing spectrograms over the speech signals to feed the CNNs with single channel inputs.…”

Section: Introductionmentioning

confidence: 99%

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

Arias-Vergara

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

Gollwitzer

et al. 2019

Self Cite

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.