Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words
Bibliographic record
Abstract
Hi,KIA dataset is a shared short Wakeup Word database focusing on perceived emotion in speech The dataset contains 488 Wakeup Word speech. For more detailed information about the dataset, please refer to our paper: Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words File Description wav/: wav files. Filename f`{gender}_{pid}_{scene}_{trial}_{emotion}.wav` The first letter was used to express emotion. annotation/: Information related to annotation and human validation of the entire speech split: 8fold data split with {train, valid, test}.csv handcraft: Features used for data EDA and baseline performance best_weights: wav2vec2.0 context network finetuning weights for re-implementation. Due to file size, we attach only fold M1, F5 Reference Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words [[ArXiv](https://arxiv.org/abs/2211.03371)] ``` @inproceedings{kim2022hi, title={Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words}, author={Taesu Kim, SeungHeon Doh, Gyunpyo Lee, Hyung seok Jun, Juhan Nam, Hyeon-Jeong Suk}, booktitle={Proceedings of the 14th Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)}, year={2022} } ```
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.043 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".