Titlebook: Automatic Assessment of Parkinsonian Speech; First Workshop, AAPS Juan I. Godino-Llorente Conference proceedings 2020 Springer Nature Switz

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書目名稱Automatic Assessment of Parkinsonian Speech影響因子(影響力)




書目名稱Automatic Assessment of Parkinsonian Speech影響因子(影響力)學(xué)科排名




書目名稱Automatic Assessment of Parkinsonian Speech網(wǎng)絡(luò)公開度




書目名稱Automatic Assessment of Parkinsonian Speech網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Automatic Assessment of Parkinsonian Speech被引頻次




書目名稱Automatic Assessment of Parkinsonian Speech被引頻次學(xué)科排名




書目名稱Automatic Assessment of Parkinsonian Speech年度引用




書目名稱Automatic Assessment of Parkinsonian Speech年度引用學(xué)科排名




書目名稱Automatic Assessment of Parkinsonian Speech讀者反饋




書目名稱Automatic Assessment of Parkinsonian Speech讀者反饋學(xué)科排名

細(xì)頸瓶

Automatic Processing of Aerodynamic Parameters in Parkinsonian Dysarthria,indicator of Parkinsonian dysarthria that could be taken into account to improve the prediction of the UPDRS score from speech signals. We also present a segmentation method applicable to pathological speech based on the measurement of RMS intensity and intra-oral pressure. This method could improve

finale

Approaches to Evaluate Parkinsonian Speech Using Artificial Models,ligence techniques used, but also to extract from them significant knowledge that might be of interest for our understanding of the effects of the disease on the speech. To this respect a phonemic analysis based on the automatic techniques developed is carried out, suggesting important cues about th

滔滔不絕地講

,Predicting UPDRS Scores in Parkinson’s Disease Using Voice Signals: A Deep Learning/Transfer-Learniries of well-know features that are used to characterise vocal conditions are employed to train a DNN. Likewise, the feature learning approach is based on transformation of the input speech using Modulation spectra transformations to train a CNN, considering a transfer learning approach. For transfe

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遺產(chǎn)

,Sch?nheit und das Gehirn: Neuro?sthetik,indicator of Parkinsonian dysarthria that could be taken into account to improve the prediction of the UPDRS score from speech signals. We also present a segmentation method applicable to pathological speech based on the measurement of RMS intensity and intra-oral pressure. This method could improve

Hirsutism

https://doi.org/10.1007/978-3-322-85500-8ligence techniques used, but also to extract from them significant knowledge that might be of interest for our understanding of the effects of the disease on the speech. To this respect a phonemic analysis based on the automatic techniques developed is carried out, suggesting important cues about th

cardiac-arrest

Praktische Hinweise zum Gelenkschutz,ries of well-know features that are used to characterise vocal conditions are employed to train a DNN. Likewise, the feature learning approach is based on transformation of the input speech using Modulation spectra transformations to train a CNN, considering a transfer learning approach. For transfe

口訣

Weniger Recht durch mehr Gesetz?uate or document treatment effects, track disease progression, or to attempt remote automatic diagnosis. These studies have often had disappointing results, so that the best way to apply acoustics to Parkinsonian voice remains an open question. In this paper we argue that past approaches have not li

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