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1
Emotional Speech Recognition Using Deep Neural Networks
In: ISSN: 1424-8220 ; Sensors ; https://hal.archives-ouvertes.fr/hal-03632853 ; Sensors, MDPI, 2022, 22 (4), pp.1414. ⟨10.3390/s22041414⟩ (2022)
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2
Emotional Speech Recognition Using Deep Neural Networks
In: Sensors; Volume 22; Issue 4; Pages: 1414 (2022)
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3
Translate Wisely! An Evaluation of Close and Adaptive Translation Procedures in an Experiment Involving Questionnaire Translation
In: International journal of sociology ; 51 ; 2 ; 135-162 (2022)
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4
Generating Samples of Diasporic Minority Popula­tions: A Chilean Example
In: Target­ing Inter­national Audiences: Current and Future Approaches to Inter­national Broad­casting Research ; 3 ; CIBAR Proceedings ; 138-149 ; Conference of International Broadcasters' Audience Research Services (CIBAR) ; XX (2022)
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5
The Optimism-Pessimism Short Scale-2 (SOP2): a comprehensive validation of the English-language adaptation
In: Measurement Instruments for the Social Sciences ; 4 ; 1-14 (2022)
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6
Warum wir so wenig über die Sprachen in Deutschland wissen: Spracheinstellungen als Erkenntnisbarriere
In: Diskurs Kindheits- und Jugendforschung / Discourse. Journal of Childhood and Adolescence Research ; 16 ; 4 ; 403-419 ; Perspektiven von Kindern und Jugendlichen auf sprachliche Diversität und Sprachbildungsprozesse (2021)
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7
Afterword: Future Directions in Multinational, Multiregional, and Multicultural (3MC) Survey Research
In: The essential role of language in survey research ; 243-256 (2021)
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8
Mapping the linguistic landscapes of the Marshall Islands
In: Journal of Linguistic Geography ; 5 ; 2 ; 67-85 (2021)
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9
Multi-mode question pretesting: Using traditional cognitive interviews and online testing as complementary methods
In: Survey Methods: Insights from the Field ; 1-14 ; Advancements in Online and Mobile Survey Methods (2021)
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10
Discourse Networks and Dual Screening: Analyzing Roles, Content and Motivations in Political Twitter Conversations
In: Politics and Governance ; 8 ; 2 ; 311-325 ; Policy Debates and Discourse Network Analysis (2021)
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11
Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets
In: Politics and Governance ; 8 ; 2 ; 326-339 ; Policy Debates and Discourse Network Analysis (2021)
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12
B-SFT: Beobachtungssystem zur Erfassung von Sprachfördertechniken im Kita- und Grundschulalltag
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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13
Einleitung: Instrumente zur Erfassung institutioneller (schrift-)sprachlicher Bildung
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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14
Forschungsinstrumente im Kontext institutioneller (schrift-)sprachlicher Bildung
Gentrup, Sarah Hrsg.; Mackowiak, Katja Hrsg.; Beckerle, Christine Hrsg.. - : Verlag Julius Klinkhardt, 2020. : Bad Heilbrunn, 2020. : pedocs-Dokumentenserver/DIPF, 2020
In: Bad Heilbrunn : Verlag Julius Klinkhardt 2020, 159 S. (2020)
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15
Style-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows
Kucherenko, Taras; Henter, Gustav Eje; Beskow, Jonas. - : KTH, Tal, musik och hörsel, TMH, 2020. : KTH, Robotik, perception och lärande, RPL, 2020. : Wiley, 2020
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16
The Quality of Big Data: Development, Problems, and Possibilities of Use of Process-Generated Data in the Digital Age
In: Historical Social Research ; 45 ; 3 ; 209-243 (2020)
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17
Sprachlich-kulturelle Herausforderungen bei der qualitativen Inhaltsanalyse musikbiografischer Interviews mit chinesischen und schweizerischen Musikstudierenden
In: Forum Qualitative Sozialforschung / Forum: Qualitative Social Research ; 20 ; 3 ; 12 ; Qualitative Content Analysis I (2019)
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18
Building a Sampling Frame for Migrant Populations via an Onomastic Approach: Lesson learned from the Austrian Immigrant Survey 2016
In: Survey Methods: Insights from the Field ; 1-20 ; Probability and Nonprobability Sampling: Sampling of hard-to-reach survey populations (2019)
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19
Evaluating cross-linguistic forced alignment of conversational data in north Australian Kriol, an under-resourced language
Jones, Caroline (R8989); Li, Weicong (R19152); Almeida, Andre. - : U.S., University of Hawaii, 2019
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20
An Empirical Study on Bidirectional Recurrent Neural Networks for Human Motion Recognition
Tanisaro, Pattreeya; Heidemann, Gunther. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2018. : LIPIcs - Leibniz International Proceedings in Informatics. 25th International Symposium on Temporal Representation and Reasoning (TIME 2018), 2018
Abstract: The deep recurrent neural networks (RNNs) and their associated gated neurons, such as Long Short-Term Memory (LSTM) have demonstrated a continued and growing success rates with researches in various sequential data processing applications, especially when applied to speech recognition and language modeling. Despite this, amongst current researches, there are limited studies on the deep RNNs architectures and their effects being applied to other application domains. In this paper, we evaluated the different strategies available to construct bidirectional recurrent neural networks (BRNNs) applying Gated Recurrent Units (GRUs), as well as investigating a reservoir computing RNNs, i.e., Echo state networks (ESN) and a few other conventional machine learning techniques for skeleton-based human motion recognition. The evaluation of tasks focuses on the generalization of different approaches by employing arbitrary untrained viewpoints, combined together with previously unseen subjects. Moreover, we extended the test by lowering the subsampling frame rates to examine the robustness of the algorithms being employed against the varying of movement speed.
Keyword: Bidirectional Recurrent Neural Networks; Data processing Computer science; Echo State Networks; Human Motion Classification; Motion Capture; Recurrent Neural Networks
URN: urn:nbn:de:0030-drops-97865
URL: https://drops.dagstuhl.de/opus/volltexte/2018/9786/
https://doi.org/10.4230/LIPIcs.TIME.2018.21
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