Sandra Ottl
University of Augsburg
11 Papers
8 Citations
Sandra Ottl is an academic researcher from University of Augsburg. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 5, co-authored 11 publications.
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Papers
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 cough, COVID-19 speech, escalation & primates
Björn Schuller,Anton Batliner,Christian Bergler,Cecilia Mascolo,Jing Han,Iulia Lefter,Heysem Kaya,Shahin Amiriparian,Alice Baird,Lukas Stappen,Sandra Ottl,Maurice Gerczuk,Panagiotis Tzirakis,Chloë Brown,Jagmohan Chauhan,Andreas Grammenos,Apinan Hasthanasombat,Dimitris Spathis,Tong Xia,Pietro Cicuta,Léon J. M. Rothkrantz,Joeri A. Zwerts,Jelle Treep,Casper S. Kaandorp +23 more
- 24 Feb 2021
TL;DR: The INTERSPEECH 2021 Computational Paralinguistics Challenge as discussed by the authors addressed four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID19 Speech Sub-Challenges, a binary classification on COVID 19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Subchallenge, four species vs background need to be classified.
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Multimodal Bag-of-Words for Cross Domains Sentiment Analysis
Nicholas Cummins,Shahin Amiriparian,Sandra Ottl,Maurice Gerczuk,Maximilian Schmitt,Björn Schuller +5 more
- 15 Apr 2018
TL;DR: Key results presented indicate that using a Bag-of-Words extraction paradigm that takes into account information from both the test domain and the out of domain datasets yields gains in system performance.
52
Group-level Speech Emotion Recognition Utilising Deep Spectrum Features
Sandra Ottl,Shahin Amiriparian,Maurice Gerczuk,Vincent Karas,Björn Schuller +4 more
- 21 Oct 2020
TL;DR: The Deep Spectrum system is used to extract deep feature representations from the audio content of the 2020 EmotiW group level emotion prediction challenge data and fuse the obtained representations and predictions in a nearly and late fusion strategy to check the complementarity of the applied networks.
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•Posted Content
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates
Björn Schuller,Anton Batliner,Christian Bergler,Cecilia Mascolo,Jing Han,Iulia Lefter,Heysem Kaya,Shahin Amiriparian,Alice Baird,Lukas Stappen,Sandra Ottl,Maurice Gerczuk,Panagiotis Tzirakis,Chloë Brown,Jagmohan Chauhan,Andreas Grammenos,Apinan Hasthanasombat,Dimitris Spathis,Tong Xia,Pietro Cicuta,Léon J. M. Rothkrantz,Joeri A. Zwerts,Jelle Treep,Casper S. Kaandorp +23 more
TL;DR: The INTERSPEECH 2021 Computational Paralinguistics Challenge as mentioned in this paper addressed four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID19 Speech Sub-Challenges, a binary classification on COVID 19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Subchallenge, four species vs background need to be classified.
30
Towards cross-modal pre-training and learning tempo-spatial characteristics for audio recognition with convolutional and recurrent neural networks
Shahin Amiriparian,Maurice Gerczuk,Sandra Ottl,Lukas Stappen,Alice Baird,Lukas Koebe,Björn Schuller,Björn Schuller +7 more
TL;DR: It is shown that using the proposed deep learning paradigms, it is possible to achieve competitive performance on all datasets and demonstrate the complementarity of CRNNs and ImageNet pre-trained CNNs for acoustic classification tasks.