{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T06:07:03Z","timestamp":1749794823606,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030007935"},{"type":"electronic","value":"9783030007942"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-00794-2_49","type":"book-chapter","created":{"date-parts":[[2018,9,7]],"date-time":"2018-09-07T19:50:24Z","timestamp":1536349824000},"page":"453-461","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Phonological Posteriors and GRU Recurrent Units to Assess Speech Impairments of Patients with Parkinson\u2019s Disease"],"prefix":"10.1007","author":[{"given":"Juan Camilo","family":"V\u00e1squez-Correa","sequence":"first","affiliation":[]},{"given":"Nicanor","family":"Garcia-Ospina","sequence":"additional","affiliation":[]},{"given":"Juan Rafael","family":"Orozco-Arroyave","sequence":"additional","affiliation":[]},{"given":"Milos","family":"Cernak","sequence":"additional","affiliation":[]},{"given":"Elmar","family":"N\u00f6th","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,9,8]]},"reference":[{"issue":"2","key":"49_CR1","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"15","key":"49_CR2","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1002\/mds.22340","volume":"23","author":"CG Goetz","year":"2008","unstructured":"Goetz, C.G., et al.: Movement Disorder Society-sponsored revision of the Unified Parkinson\u2019s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. 23(15), 2129\u20132170 (2008)","journal-title":"Mov. Disord."},{"doi-asserted-by":"crossref","unstructured":"Cernak, M., Potard, B., Garner, P.N.: Phonological vocoding using artificial neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, pp. 4844\u20134848. IEEE (2015)","key":"49_CR3","DOI":"10.1109\/ICASSP.2015.7178891"},{"key":"49_CR4","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.csl.2017.06.004","volume":"46","author":"M Cernak","year":"2017","unstructured":"Cernak, M., et al.: Characterisation of voice quality of Parkinsons disease using differential phonological posterior features. Comput. Speech Lang. 46, 96\u2013208 (2017)","journal-title":"Comput. Speech Lang."},{"doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 1724\u20131734 (2014)","key":"49_CR5","DOI":"10.3115\/v1\/D14-1179"},{"unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Deep Learning and Representation Learning Workshop (2014)","key":"49_CR6"},{"issue":"12","key":"49_CR7","first-page":"1","volume":"7","author":"J Hlavnicka","year":"2017","unstructured":"Hlavnicka, J., Cmejla, R., Tykalova, T., Sonka, K., Ruzicka, E., Rusz, J.: Automated analysis of connected speech reveals early biomarkers of Parkinson\u2019s disease in patients with rapid eye movement sleep behaviour disorder. Nat. Sci. Rep. 7(12), 1\u201313 (2017)","journal-title":"Nat. Sci. Rep."},{"issue":"2 Suppl. 2","key":"49_CR8","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1212\/WNL.51.2_Suppl_2.S2","volume":"51","author":"O Hornykiewicz","year":"1998","unstructured":"Hornykiewicz, O.: Biochemical aspects of Parkinson\u2019s disease. Neurology 51(2 Suppl. 2), S2\u2013S9 (1998)","journal-title":"Neurology"},{"doi-asserted-by":"crossref","unstructured":"Irie, K., T\u00fcske, Z., Alkhouli, T., Schl\u00fcter, R., Ney, H.: LSTM, GRU, highway and a bit of attention: an empirical overview for language modeling in speech recognition. In: Proceedings of INTERSPEECH, pp. 3519\u20133523 (2016)","key":"49_CR9","DOI":"10.21437\/Interspeech.2016-491"},{"issue":"7553","key":"49_CR10","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"1","key":"49_CR11","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1044\/jshd.4301.47","volume":"43","author":"JA Logemann","year":"1978","unstructured":"Logemann, J.A., Fisher, H.B., Boshes, B., Blonsky, E.R.: Frequency and cooccurrence of vocal tract dysfunctions in the speech of a large sample of Parkinson patients. J. Speech Hear. Disord. 43(1), 47\u201357 (1978)","journal-title":"J. Speech Hear. Disord."},{"doi-asserted-by":"crossref","unstructured":"Orozco-Arroyave, J.R., V\u00e1squez-Correa, J.C., et al.: NeuroSpeech: an open-source software for Parkinson\u2019s speech analysis. Dig. Signal Process. (2017, in press)","key":"49_CR12","DOI":"10.1016\/j.dsp.2017.07.004"},{"unstructured":"Orozco-Arroyave, J.R., et al.: New Spanish speech corpus database for the analysis of people suffering from Parkinson\u2019s disease. In: Language Resources and Evaluation Conference, LREC, pp. 342\u2013347 (2014)","key":"49_CR13"},{"unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, NIPS, pp. 2951\u20132959 (2012)","key":"49_CR14"},{"doi-asserted-by":"crossref","unstructured":"Tu, M., Berisha, V., Liss, J.: Interpretable objective assessment of dysarthric speech based on deep neural networks. In: Proceedings of INTERSPEECH, pp. 1849\u20131853 (2017)","key":"49_CR15","DOI":"10.21437\/Interspeech.2017-1222"},{"doi-asserted-by":"crossref","unstructured":"V\u00e1squez-Correa, J.C., Orozco-Arroyave, J.R., N\u00f6th, E.: Convolutional neural network to model articulation impairments in patients with Parkinson\u2019s disease. In: Proceedings of INTERSPEECH, pp. 314\u2013318 (2017)","key":"49_CR16","DOI":"10.21437\/Interspeech.2017-1078"}],"container-title":["Lecture Notes in Computer Science","Text, Speech, and Dialogue"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00794-2_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:38:30Z","timestamp":1709829510000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00794-2_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030007935","9783030007942"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00794-2_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"8 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}