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Forschungsinstrumente im Kontext institutioneller (schrift-)sprachlicher Bildung ...
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null. - : Verlag Julius Klinkhardt, 2020
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Einleitung: Instrumente zur Erfassung institutioneller (schrift-)sprachlicher Bildung ...
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B-SFT: Beobachtungssystem zur Erfassung von Sprachfördertechniken im Kita- und Grundschulalltag ...
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B-SFT: Beobachtungssystem zur Erfassung von Sprachfördertechniken im Kita- und Grundschulalltag
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In: Mackowiak, Katja [Hrsg.]; Beckerle, Christine [Hrsg.]; Gentrup, Sarah [Hrsg.]; Titz, Cora [Hrsg.]: Forschungsinstrumente im Kontext institutioneller (schrift-)sprachlicher Bildung. Bad Heilbrunn : Verlag Julius Klinkhardt 2020, S. 79-101 (2020)
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Einleitung: Instrumente zur Erfassung institutioneller (schrift-)sprachlicher Bildung
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In: Mackowiak, Katja [Hrsg.]; Beckerle, Christine [Hrsg.]; Gentrup, Sarah [Hrsg.]; Titz, Cora [Hrsg.]: Forschungsinstrumente im Kontext institutioneller (schrift-)sprachlicher Bildung. Bad Heilbrunn : Verlag Julius Klinkhardt 2020, S. 7-12 (2020)
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Forschungsinstrumente im Kontext institutioneller (schrift-)sprachlicher Bildung
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In: Bad Heilbrunn : Verlag Julius Klinkhardt 2020, 159 S. (2020)
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"Langsam vermisse ich die Schule .". Schule während und nach der Corona-Pandemie
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In: Münster ; New York : Waxmann 2020, 229 S. - (Die Deutsche Schule, Beiheft; 16) (2020)
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Non-native student and supervisor expectations during the process of completing a graduation thesis: A case study
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In: Eesti Rakenduslingvistika Ühingu Aastaraamat, Vol 14, Pp 5-21 (2018) (2018)
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Authentizität und Lernen - das Fach in der Fachdidaktik. Gesellschaft für Didaktik der Chemie und Physik Jahrestagung in Berlin 2015
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Maurer, Christian Hrsg.. - : Universität Regensburg, 2016. : Regensburg, 2016. : pedocs-Dokumentenserver/DIPF, 2016
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In: Regensburg : Universität Regensburg 2016, 610 S. - (Gesellschaft für Didaktik der Chemie und Physik; 36) (2016)
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The Effect of Tactile and Audio Feedback in Handheld Mobile Text Entry
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In: Browse all Theses and Dissertations (2016)
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Feedback on feedback – does it work?
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In: Helm, Francesca [Hrsg.]; Bradley, Linda [Hrsg.]; Guarda, Marta [Hrsg.]; Thouësny, Sylvie [Hrsg.]: Critical CALL. Proceedings of the 2015 EUROCALL conference, Padova, Italy. Dublin : Research-publishing.net 2015, S. 507-511 (2015)
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Postgraduate students’ perception of creativity in the research process
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In: CEPS Journal 1 (2011) 1, S. 169-190 (2011)
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Jörg Ramseger / Matthea Wagener (Hrsg.): Chancenungleichheit in der Grundschule. Ursachen und Wege aus der Krise (Jahrbuch Grundschulforschung, Band 12). Wiesbaden: VS Verlag 2008 (306 S.) [Rezension]
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In: Erziehungswissenschaftliche Revue (EWR) 8 (2009) 1 (2009)
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Virtualisierung der Schulpraxis an der Pädagogischen Hochschule Weingarten ...
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Virtualisierung der Schulpraxis an der Pädagogischen Hochschule Weingarten
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In: Carstensen, Doris [Hrsg.]; Barrios, Beate [Hrsg.]: Campus 2004. Kommen die digitalen Medien an den Hochschulen in die Jahre? Münster u. a. : Waxmann 2004, S. 214-224. - (Medien in der Wissenschaft; 29) (2004)
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The lexicographic treatment of days in Sepedi, or When mother-tongue intuition fails
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In: http://tshwanedje.com/publications/days.pdf (2001)
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An Approach to Handwriting Recognition using Back-Propagation Neural Network
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In: http://research.ijcaonline.org/volume68/number13/pxc3887117.pdf
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DIGIT RECOGNITION USING NEURAL NETWORKS
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In: http://mjcs.fsktm.um.edu.my/document.aspx?FileName=308.pdf
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Robust Speech Emotion Recognition in the Indian Language: “Telugu ” based on Feed Forward Back Propagation Neural Network.
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In: http://ijnngt.org/upload/journal5vl2/js14.pdf
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Abstract:
Abstract:-Speech processing is the study of speech signals, and the methods used to process them. In application such as speech coding, speech synthesis, speech recognition and speaker recognition technology, speech processing is employed. In speech classification, the computation of prosody effects from speech signals plays a major role. In emotional speech signals pitch and frequency is a most important parameters. Normally, the pitch value of sad and happy speech signals has a great difference and the frequency value of happy is higher than sad speech. But, in some cases the frequency of happy speech is nearly similar to sad speech or frequency of sad speech is similar to happy speech. In such situation, it is difficult to recognize the exact speech signal. To reduce such drawbacks, in this paper we propose a Telugu speech emotion classification system with three features and use neural network for the classification. Features are extracted with optimal window size from the speech signals and given to the FFBNN. The well trained FFBNN is tested with more number of speech signals with prosody effects. The implementation result shows the effectiveness of proposed speech emotion classification system in classifying the Telugu speech signals based on their prosody effects. The performance of the proposed speech emotion classification system is evaluated by change the learning error rate of the Back Propagation Network till the recognition rate reaches to optimum value.
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Keyword:
Energy Entropy; Feed Forward Back Propagation Neural Network (FFBNN; Learning Rate; Short Time Energy; Zero Crossing Rate
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.679.6498 http://ijnngt.org/upload/journal5vl2/js14.pdf
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