Juan R. Orozco-Arroyave

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The INTERSPEECH 2015 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well-defined conditions: the estimation of the degree of nativeness, the neurological state of patients with Parkinson’s condition, and the eating conditions of speakers, i. e., whether and which food type they are(More)
Parkinsons disease (PD) is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting about 1% of the people older than 65 and about 89% of the people with PD develop different speech disorders. Different researchers are currently working in the analysis of speech of people with PD, including the study of different dimensions in(More)
Different characterization approaches, including nonlinear dynamics (NLD), have been addressed for the automatic detection of PD; however, the obtained discrimination capability when only NLD features are considered has not been evaluated yet. This paper evaluates the discrimination capability of a set with ten different NLD features in the task of(More)
The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into voiced and unvoiced frames, is addressed here. The energy(More)
About 90% of the people with Parkinson’s disease (PD) develop speech impairments such as monopitch, monoloudness, imprecise articulation, and other symptoms. There are several studies addressing the problem of the automatic detection of PD from speech signals in order to develop computer aided tools for the assessment and monitoring of the patients. Recent(More)
In this paper, the analysis of low-frequency zone of the speech signals from the five Spanish vowels, by means of the Teager energy operator (TEO) and the modified group delay functions (MGDF) is proposed for the automatic detection of Parkinson’s disease. According to our findings, different implementations of the TEO are suitable for tackling the problem(More)
Automatic classification of Parkinson’s disease (PD) speakers and healthy controls (HC) is performed considering speech recordings collected in non-controlled noise conditions. The speech tasks include six sentences and a read text. The recording is performed using an open source portable device and a commercial microphone. A speech enhancement (SE)(More)
Parkinson’s disease (PD) is a neurodegenerative disorder of the nervous central system and it affects the limbs motor control and the communication skills of the patients. The evolution of the disease can get to the point of affecting the intelligibility of the patient’s speech. The treatments of the PD are mainly focused on improving limb symptoms and(More)
Several studies have addressed the automatic classification of speakers with Parkinson’s disease (PD) and healthy controls (HC). Most of the studies are based on speech recordings of sustained vowels, isolated words, and single sentences. Only few investigations have considered read texts and/or spontaneous speech. This paper addresses two main questions(More)
Parkinson’s disease (PD) is a chronic neurodegenerative disorder of the nervous central system and it can affect the communication skills of the patients. There is an interest in the research community to develop computer aided tools for the analysis of the speech of people with PD for detection and monitoring. In this paper, three new acoustic measures for(More)