| 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)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Emotional Speech Recognition Using Deep Neural Networks
|
|
|
|
In: Sensors; Volume 22; Issue 4; Pages: 1414 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Afterword: Future Directions in Multinational, Multiregional, and Multicultural (3MC) Survey Research
|
|
|
|
In: The essential role of language in survey research ; 243-256 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Mapping the linguistic landscapes of the Marshall Islands
|
|
|
|
In: Journal of Linguistic Geography ; 5 ; 2 ; 67-85 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Forschungsinstrumente im Kontext institutioneller (schrift-)sprachlicher Bildung
|
|
|
|
In: Bad Heilbrunn : Verlag Julius Klinkhardt 2020, 159 S. (2020)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Style-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows
|
|
Kucherenko, Taras; Henter, Gustav Eje; Beskow, Jonas; Alexanderson, Simon. - : KTH, Tal, musik och hörsel, TMH, 2020. : KTH, Robotik, perception och lärande, RPL, 2020. : Wiley, 2020
|
|
Abstract:
Automatic synthesis of realistic gestures promises to transform the fields of animation, avatars and communicative agents. In off-line applications, novel tools can alter the role of an animator to that of a director, who provides only high-level input for the desired animation; a learned network then translates these instructions into an appropriate sequence of body poses. In interactive scenarios, systems for generating natural animations on the fly are key to achieving believable and relatable characters. In this paper we address some of the core issues towards these ends. By adapting a deep learning-based motion synthesis method called MoGlow, we propose a new generative model for generating state-of-the-art realistic speech-driven gesticulation. Owing to the probabilistic nature of the approach, our model can produce a battery of different, yet plausible, gestures given the same input speech signal. Just like humans, this gives a rich natural variation of motion. We additionally demonstrate the ability to exert directorial control over the output style, such as gesture level, speed, symmetry and spacial extent. Such control can be leveraged to convey a desired character personality or mood. We achieve all this without any manual annotation of the data. User studies evaluating upper-body gesticulation confirm that the generated motions are natural and well match the input speech. Our method scores above all prior systems and baselines on these measures, and comes close to the ratings of the original recorded motions. We furthermore find that we can accurately control gesticulation styles without unnecessarily compromising perceived naturalness. Finally, we also demonstrate an application of the same method to full-body gesticulation, including the synthesis of stepping motion and stance. ; QC 20211011
|
|
Keyword:
Animation; CCS Concepts; Character control; Computer Sciences; Computing methodologies; Data-driven animation; Datavetenskap (datalogi); Gestures; Human Computer Interaction; Language Technology (Computational Linguistics); Människa-datorinteraktion (interaktionsdesign); Motion capture; Neural networks; Probabilistic models; Språkteknologi (språkvetenskaplig databehandling); WASP_publications
|
|
URL: https://doi.org/10.1111/cgf.13946
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279231
|
|
BASE
|
|
Hide details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 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)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Evaluating cross-linguistic forced alignment of conversational data in north Australian Kriol, an under-resourced language
|
|
|
|
BASE
|
|
Show details
|
|
|
| 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
|
|
BASE
|
|
Show details
|
|
|
|
|
