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MULTIPLE EFFECTIVENESS CRITERIA OF FORMING DATABASES OF EMOTIONAL VOICE SIGNALS

2023· article· en· W4387402209 on OpenAlexaboutno aff
Ivan Dychka, Ihor Tereikovskyi, Andrii Samofalov, Lyudmila Tereykovska, Vitaliy A. Romankevich

Bibliographic record

VenueCybersecurity Education Science Technique · 2023
Typearticle
Languageen
FieldPsychology
TopicEmotion and Mood Recognition
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)DatabaseAffective computingComputer scienceSpeech recognitionPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Ekman, P. (2005). Basic Emotions. In Handbook of Cognition and Emotion (p. 45–60). John Wiley & Sons, Ltd. https://doi.org/10.1002/0470013494.ch3 Bachorowski, J.-A., & Owren, M. J. (1995). Vocal Expression of Emotion: Acoustic Properties of Speech Are Associated With Emotional Intensity and Context. Psychological Science, 6(4), 219–224. https://doi.org/10.1111/j.1467-9280.1995.tb00596.x Hirschberg, J. (2006). Pragmatics and Intonation. In The Handbook of Pragmatics (eds L.R. Horn and G. Ward). https://doi.org/10.1002/9780470756959.ch23 Tereykovska, L. (2023). Methodology of automated recognition of the emotional state of listeners of the distance learning system [Dissertation, Kyiv National University of Construction and Architecture]. Institutional repository of National transport university. http://www.ntu.edu.ua/nauka/oprilyudnennya-disertacij/ Kominek, J., & Black, A. (2004). The CMU Arctic speech databases. SSW5-2004. https://www.lti.cs.cmu.edu/sites/default/files/CMU-LTI-03-177-T.pdf (date of access: 01.06.2023) Zhou, K., Sisman, B., Liu, R., & Li, H. (2022). Emotional voice conversion: Theory, databases and ESD. Speech Communication, 137, 1–18. https://doi.org/10.1016/j.specom.2021.11.006 Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., & Weiss, B. (2005). A database of German emotional speech. In Interspeech 2005. ISCA. https://doi.org/10.21437/interspeech.2005-446 Livingstone, S. R., & Russo, F. A. (2018). The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLOS ONE, 13(5), Стаття e0196391. https://doi.org/10.1371/journal.pone.0196391 James, J., Tian, L., & Inez Watson, C. (2018). An Open Source Emotional Speech Corpus for Human Robot Interaction Applications. In Interspeech 2018. ISCA. https://doi.org/10.21437/interspeech.2018-1349 10) Costantini, G., Iaderola, I., Paoloni, A., & Todisco, M. (2014). EMOVO Corpus: an Italian Emotional Speech Database. У Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), 3501–3504, Reykjavik, Iceland. European Language Resources Association (ELRA).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.423
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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