Automatic Detection of Parkinson’s Disease from Compressed Speech Recordings

Orozco-Arroyave, Juan Rafael; Garcia, N.; Vargas-Bonilla, Jesús Francisco; Nöth, Elmar

doi:10.1007/978-3-319-24033-6_10

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…[33], we developed an algorithm to recognize certain regions on the emotional plane specialized on sadness and in Ref. [34], an algorithm for Parkinson's disease detection and monitoring was proposed. Another practical use is the monitoring of telephone interactions, i.e., call center, [35].…”

Section: Voice Speech Emotion Recognitionmentioning

confidence: 99%

Improving Quality of Life: Home Care for Chronically Ill and Elderly People

Celani¹,

Ponce²,

Quintero³

et al. 2018

Caregiving and Home Care

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In this chapter, we propose a system especially created for elderly or chronically ill people that are with special needs and poor familiarity with technology. The system combines home monitoring of physiological and emotional states through a set of wearable sensors, user-controlled (automated) home devices, and a central control for integration of the data, in order to provide a safe and friendly environment according to the limited capabilities of the users. The main objective is to create the easy, low-cost automation of a room or house to provide a friendly environment that enhances the psychological condition of immobilized users. In addition, the complete interaction of the components provides an overview of the physical and emotional state of the user, building a behavior pattern that can be supervised by the care giving staff. This approach allows the integration of physiological signals with the patient's environmental and social context to obtain a complete framework of the emotional states.

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Section: Voice Speech Emotion Recognitionmentioning

confidence: 99%

Improving Quality of Life: Home Care for Chronically Ill and Elderly People

Celani¹,

Ponce²,

Quintero³

et al. 2018

Caregiving and Home Care

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“…Samples are compressed using, G.722, G.226, GSM-EFR, AMR-WB, /SILK, and OPUS. All these compression methods are being used in telecommunication and VOIP solutions to transfer the voice signals over internet or data links, [1,2,6] . A software […”

mentioning

confidence: 99%

Parkinson’s Diagnosis through Compressed Speech Signals at remote Locations

Pervaiz¹,

Tahir²

2020

PIJ

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Rapid increase in Parkinson’s disease all over the world before the age of 60’s is alarming. Parkinson’s affects the neurological controls of human body. In this paper we intend to adopt machine based learning to automate the diagnosis of Parkinson’s disease with help of compressed speech signals sent via network. An advanced Solution is proposed to automate the diagnosis process more rapid than that of ordinary process. In this study Speech samples from 55 subjects have been collected, 43 with Parkinson Disease and 12 Healthy subjects. Speech signals from clients captured from various sensors and devices transmitted to the cloud for processing. In the cloud Speech samples are compressed using compression codecs MP3, MP4, G.722, G.226, GSM-EFR, AMR-WB, SVOPC/SILK, and OPUS, and then same are diagnosed without compression impact and deliver accurate results. Three classifiers SVM (Support Vector Machine), NN (Neural Network), and GA (Genetic Algorithm) are applied and calculated accurate results as compared with compressed and un-compressed voice samples. Successful experiments enabled us to achieve 90.7% accuracy. Same process can be used for online and social media applications which are available in portable devices as well.

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