The INTERSPEECH 2012 speaker trait challenge
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The INTERSPEECH 2012 Speaker Trait Challenge provides for the first time a unified test-bed for 'perceived' speaker traits: Personality in the five OCEAN personality dimensions, likability of speakers, and intelligibility of pathologic speakers.In this paper, we describe these three Sub-Challenges, Challenge conditions, baselines, and a new feature set by the openSMILE toolkit, provided to the participants.
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The number of features per utterance varies from several hundred to several thousand, depending on the employed LLDs and HSFs.
“This number varies from several hundred [157] to several thousand [158], [123], depending on the employed LLDs and HSFs.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [158]
The number of features per utterance varies from several hundred to several thousand, depending on the employed LLDs and HSFs.
“This number varies from several hundred [157] to several thousand [158], [123], depending on the employed LLDs and HSFs.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [123]
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Polzehl, T., Schoenenberg, K., Möller, S., Metze, F., Mohammadi, G., & Vinciarelli, A. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-87 · Cited by 17 · see citing works
Self-citationAutomatic characterization of speaking styles in educational videos
Mariooryad, S., Kannan, A., Hakkani-Tur, D. Z., & Shriberg, E. E. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854523 · Cited by 17 · see citing works
Improving Speech Recognition through Automatic Selection of Age Group – Specific Acoustic Models
Hämäläinen, A., Meinedo, H., Tjalve, M., Pellegrini, T., Trancoso, I. M., & Dias, M. S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09761-9_2 · Cited by 16 · see citing works
Is 'not bad' good enough? aspects of unknown voices' likability
Weiß, B., & Burkhardt, F. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-97 · Cited by 16 · see citing works
Self-citationMultimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions
Kaya, H., & Salah, A. A. (2018) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-98131-4_10 · Cited by 16 · see citing works
You're Not You When You're Angry: Robust Emotion Features Emerge by Recognizing Speakers
Aldeneh, Z., & Provost, E. M. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3086050 · Cited by 16 · see citing works
Detecting Speech Interruptions for Automatic Conflict Detection
Caraty, M.-J., & Montacié, C. (2015) · Springer eBooks · doi:10.1007/978-3-319-14081-0_18 · Cited by 15 · see citing works
Hierarchical neural networks and enhanced class posteriors for social signal classification
Brueckner, R., & Schuller, B. W. (2013) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2013.6707757 · Cited by 14 · see citing works
Self-citationChen, X., Han, W., Ruan, H., Liu, J., Li, H., & Jiang, D. (2018) · Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) · doi:10.1109/aciiasia.2018.8470325 · Cited by 14 · see citing works
Personality trait estimation in group discussions using multimodal analysis and speaker embedding
Mawalim, C. O., Okada, S., Nakano, Y., & Unoki, M. (2023) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-023-00401-0 · Cited by 14 · see citing works
Gupta, R., Audhkhasi, K., Lee, S., & Narayanan, S. S. (2015) · Computer Speech & Language · doi:10.1016/j.csl.2015.08.003 · Cited by 15 · see citing works
Transfer Learning of Deep Neural Network for Speech Emotion Recognition
Huang, Y., Hu, M., Yu, X., Wang, T., & Yang, C. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_59 · Cited by 14 · see citing works
MES-P: An Emotional Tonal Speech Dataset in Mandarin with Distal and Proximal Labels
Xiao, Z., Chen, Y., Dou, W., Tao, Z., & Chen, L. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2945322 · Cited by 15 · see citing works
Automatic Personality Recognition from reading text speech
Mohsen, F., Vali, M., & Khalili, M. (2017) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2017.7985447 · Cited by 13 · see citing works
- Authors
- Schuller, B., Steidl, S., Batliner, A., Nöth, E., Vinciarelli, A., Burkhardt, F., Son, R. V., Weninger, F., Eyben, F., Bocklet, T., Mohammadi, G., & Weiss, B.
- Affiliations
- Technical University of Munich (DE)
- MorphoSys (Germany) (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- University of Glasgow (GB)
- Deutsche Telekom (United Kingdom) (GB)
- École Polytechnique Fédérale de Lausanne (CH)
- Technische Universität Berlin (DE)
- ORCID
- Venue
- Interspeech 2012
- Publisher
- ISCA
- Published
- 2012-09-09
- Pages
- 254-257
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W1501506202
- Cited by
- 211
- References
- 13
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 4 · 2023: 9 · 2022: 2 · 2021: 8 · 2020: 19 · 2019: 19 · 2018: 15 · 2017: 14 · 2016: 19 · 2015: 30 · 2014: 21 · 2013: 26 · 2012: 18
- FWCI
- 23.30
- Type
- conference paper
- Language
- en
- Open access
- green
- mediatum.ub.tum.de
- Licence
- other-oa
- Locations
- Interspeech (USB) (publishedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- OPUS (Augsburg University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- European Commission (289021)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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