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3 results for “speech emotion recognition”

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zenodo40/100

Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words

<p>Hi,KIA dataset is a shared short Wakeup Word&nbsp;database focusing on perceived emotion in&nbsp;speech The dataset contains&nbsp;<strong>488 </strong>Wakeup Word&nbsp;speech.&nbsp;</p> <p>For more detailed information about the dataset, please refer to our paper:&nbsp;Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words</p> <p><strong>File Description</strong></p> <ul> <li><em><strong>wav/</strong></em>:&nbsp;wav files. <ul> <li>Filename f`{gender}_{pid}_{scene}_{trial}_{emotion}.wav`&nbsp;The first letter was used to express emotion.<br> &nbsp;</li> </ul> </li> <li><em><strong>annotation/</strong></em>:&nbsp;Information related to annotation and human validation of the entire speech</li> <li> <p><em><strong>split</strong></em>: 8fold data split with {train, valid, test}.csv&nbsp;</p> </li> <li> <p><em><strong>handcraft:</strong></em>&nbsp;Features used for data EDA and baseline performance</p> </li> <li> <p><em><strong>best_weights:</strong></em>&nbsp;wav2vec2.0 context network finetuning weights for re-implementation. Due to file size, we attach only fold M1, F5</p> </li> </ul> <p>&nbsp;</p> <p><strong>Reference</strong></p> <ul> </ul> <p>Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words [[ArXiv](https://arxiv.org/abs/2211.03371)]</p> <p>```<br> @inproceedings{kim2022hi,<br> &nbsp; title={Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words},<br> &nbsp; author={Taesu Kim, SeungHeon Doh, Gyunpyo Lee, Hyung seok Jun, Juhan Nam, Hyeon-Jeong Suk},<br> &nbsp; booktitle={Proceedings of the 14th Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)},<br> &nbsp; year={2022}<br> }<br> ```</p>

opencc-by-4.0Aug 2022View details →
zenodo40/100

Appendix: Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition

<p>Appendix tables for the paper &quot;Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition&quot;.</p> <p>Feature informativeness information was gathered using SHAP values.</p> <p>TABLE VIII: Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE IX: (continue)Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE X: Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XI: (continue) Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XII: Table of 5 the most informative feature for each model, according to SHAP values</p> <p>TABLE XIII: (continue)Table of 5 the most informative feature for each model, according to SHAP values</p>

opencc-by-4.0Apr 2023View details →
zenodo36/100

Appendix - Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition

<p>Appendix tables for the paper &quot;Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition&quot;.</p> <p>Feature informativeness information was gathered using SHAP values.</p> <p>TABLE VIII: Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE IX: (continue)Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE X: Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XI: (continue) Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XII: Table of 5 the most informative feature for each model, according to SHAP values</p> <p>TABLE XIII: (continue)Table of 5 the most informative feature for each model, according to SHAP values</p>

opencc-by-4.0Jul 2023View details →

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International Brain Laboratory public data

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Last verified 2026-04-29Open record