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<!DOCTYPE html>
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content="Multi-Corpus Emotion Recognition Method based on Cross-Modal Gated Attention Fusion">
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<title>Multi-Corpus Emotion Recognition Method based on Cross-Modal Gated Attention Fusion</title>
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<h1 class="title is-1 publication-title">
Multi-Corpus Emotion Recognition Method based on Cross-Modal Gated Attention Fusion
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://hci.nw.ru/en/employees/14" target="_blank">Elena Ryumina</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://hci.nw.ru/en/employees/3" target="_blank">Dmitry Ryumin</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://hci.nw.ru/en/employees/9" target="_blank">Alexandr Axyonov</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://hci.nw.ru/en/employees/4" target="_blank">Denis Ivanko</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://hci.nw.ru/en/employees/1" target="_blank">Alexey Karpov</a><sup>2</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup> St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, <a href="https://spcras.ru/en/" target="_blank">St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS)</a>, St. Petersburg, Russia</span>
<span class="author-block"><sup>2</sup> <a href="https://en.itmo.ru/" target="_blank">ITMO University</a>, St. Petersburg, Russia</span>
<br />
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<h2 class="title is-3">Abstract</h2>
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<p>
Automatic emotion recognition methods are critical to human-computer interaction. However, current methods suffer from limited applicability due to their tendency to overfit on single-corpus datasets. This overfitting reduces real-world effectiveness of the methods when faced with new unseen corpora. We propose the first multi-corpus multimodal emotion recognition method with high generalizability evaluated through a leave-one-corpus-out protocol. The method uses three fine-tuned encoders per modality (audio, video, and text) and a decoder employing context-independent gated attention to combine features from all three modalities. The research is conducted on four benchmark corpora: MOSEI, MELD, IEMOCAP, and AFEW. The proposed method achieves the state-of-the-art results on the corpora and establishes the first baselines for multi-corpus studies.
We demonstrate that due to the MELD rich emotional expressiveness across three modalities, the models trained on it exhibit the best generalization ability when applied to corpora used.
We also reveal that the AFEW annotation better correlates with the annotations of MOSEI, MELD, IEMOCAP and shows the best cross-corpus performance as it is consistent with the widely-accepted concepts of basic emotions.
</p>
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<h6 class="title is-6">The statistics of research corpora. Acc refers to Accuracy, wAcc to Weighted Accuracy, F1 to F1-score, UAR to Unweighted Average Recall.</h6>
<table>
<thead>
<tr>
<th>Corpus</th>
<th>Dur., h</th>
<th>Dur. range, sec (Min / Max / Mean)</th>
<th>Train</th>
<th>Val.</th>
<th>Test</th>
<th>Classes of emotions</th>
<th>Class imbalance (Mean / STD)</th>
<th>Evaluation measures</th>
</tr>
</thead>
<tbody>
<tr>
<td>IEMOCAP</td>
<td>9:24</td>
<td>0.6 / 34.1 / 4.6</td>
<td>5229</td>
<td>581</td>
<td>1623</td>
<td>6</td>
<td>959.7 / 344.4</td>
<td>Acc / F1 / UAR</td>
</tr>
<tr>
<td>MOSEI</td>
<td>45:51</td>
<td>0.2 / 108.9 / 7.2</td>
<td>16216</td>
<td>1835</td>
<td>4625</td>
<td>6</td>
<td>3734.3 / 2441.1</td>
<td>Acc / wAcc / F1 / UAR</td>
</tr>
<tr>
<td>MELD</td>
<td>12:4</td>
<td>0.2 / 304.9 / 3.2</td>
<td>9989</td>
<td>1109</td>
<td>2610</td>
<td>7</td>
<td>1426.9 / 1427.0</td>
<td>Acc / F1 / UAR</td>
</tr>
<tr>
<td>AFEW</td>
<td>0:48</td>
<td>0.5 / 6.3 / 2.5</td>
<td>773</td>
<td>383</td>
<td>--</td>
<td>7</td>
<td>110.4 / 31.1</td>
<td>Acc / F1 / UAR</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
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<h6 class="title is-6">Results (Recall, %) of two setups of single-corpus ablation based on single-corpus models trained with the weighted loss. A, V and T refer to audio, video and text modalities, respectively. ↓ and ↑ show the most and least informative modalities compared to the three-modal model performance.</h6>
<table>
<thead>
<tr>
<th>Modality</th>
<th>Neutral</th>
<th>Happy</th>
<th>Sad</th>
<th>Angry</th>
<th>Excited</th>
<th colspan="2">Frustrated</th>
<th>UAR</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="9" class="center"><b>IEMOCAP</b></td>
</tr>
<tr>
<td>A+V+T</td>
<td class="center">76.6</td>
<td class="center">51.7</td>
<td class="center">78.0</td>
<td class="center">78.8</td>
<td class="center">75.3</td>
<td colspan="2" class="center">69.6</td>
<td class="center">71.6</td>
</tr>
<tr>
<td colspan="9" class="center">Setup 1. Modality feature nullification</td>
</tr>
<tr>
<td>w/o A</td>
<td class="center">69.5</td>
<td class="center">42.7</td>
<td class="center">74.3</td>
<td class="center">60.0 ↓</td>
<td class="center">72.6</td>
<td colspan="2" class="center">68.5</td>
<td class="center">64.6</td>
</tr>
<tr>
<td>w/o V</td>
<td class="center">57.0 ↓</td>
<td class="center">58.0 ↑</td>
<td class="center">69.0</td>
<td class="center">72.4</td>
<td class="center">51.5 ↓</td>
<td colspan="2" class="center">77.4 ↑</td>
<td class="center">64.2</td>
</tr>
<tr>
<td>w/o T</td>
<td class="center">82.8 ↑</td>
<td class="center">14.7 ↓</td>
<td class="center">66.1 ↓</td>
<td class="center">77.6</td>
<td class="center">72.9</td>
<td colspan="2" class="center">3.4 ↓</td>
<td class="center">52.9 ↓</td>
</tr>
<tr>
<td colspan="9" class="center">Setup 2. Modality exclusion</td>
</tr>
<tr>
<td>w/o A</td>
<td class="center">69.3</td>
<td class="center">50.3</td>
<td class="center">77.1</td>
<td class="center">74.1</td>
<td class="center">70.2</td>
<td colspan="2" class="center">66.1</td>
<td class="center">67.9</td>
</tr>
<tr>
<td>w/o V</td>
<td class="center">66.9 ↓</td>
<td class="center">48.3 ↓</td>
<td class="center">73.9 ↓</td>
<td class="center">75.9</td>
<td class="center">65.6</td>
<td colspan="2" class="center">66.7</td>
<td class="center">66.2</td>
</tr>
<tr>
<td>w/o T</td>
<td class="center">71.9</td>
<td class="center">64.3 ↑</td>
<td class="center">85.7 ↑</td>
<td class="center">56.5 ↓</td>
<td class="center">62.2 ↓</td>
<td colspan="2" class="center">54.9 ↓</td>
<td class="center">65.9 ↓</td>
</tr>
</tbody>
<thead>
<tr>
<th>Modality</th>
<th>Neutral</th>
<th>Happy / Joy</th>
<th>Sad</th>
<th>Angry</th>
<th>Surprise</th>
<th>Fear</th>
<th>Disgust</th>
<th>UAR</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="9" class="center"><b>MOSEI</b></td>
</tr>
<tr>
<td>A+V+T</td>
<td class="center">--</td>
<td class="center">77.4</td>
<td class="center">57.3</td>
<td class="center">56.9</td>
<td class="center">48.4</td>
<td class="center">54.0</td>
<td class="center">68.9</td>
<td class="center">60.5</td>
</tr>
<tr>
<td colspan="9" class="center">Setup 1. Modality feature nullification</td>
</tr>
<tr>
<td>w/o A</td>
<td class="center">--</td>
<td class="center">72.8</td>
<td class="center">52.7 ↓</td>
<td class="center">69.7 ↑</td>
<td class="center">44.3 ↓</td>
<td class="center">52.2</td>
<td class="center">68.1</td>
<td class="center">60.0</td>
</tr>
<tr>
<td>w/o V</td>
<td class="center">--</td>
<td class="center">71.6 ↓</td>
<td class="center">53.2</td>
<td class="center">55.7</td>
<td class="center">50.7 ↑</td>
<td class="center">51.7</td>
<td class="center">72.5 ↑</td>
<td class="center">59.2</td>
</tr>
<tr>
<td>w/o T</td>
<td class="center">--</td>
<td class="center">72.2</td>
<td class="center">62.9 ↑</td>
<td class="center">45.8 ↓</td>
<td class="center">45.0</td>
<td class="center">49.9 ↓</td>
<td class="center">56.3 ↓</td>
<td class="center">55.4 ↓</td>
</tr>
<tr>
<td colspan="9" class="center">Setup 2. Modality exclusion</td>
</tr>
<tr>
<td>w/o A</td>
<td class="center">--</td>
<td class="center">75.1</td>
<td class="center">63.1 ↑</td>
<td class="center">60.3 ↑</td>
<td class="center">55.2 ↑</td>
<td class="center">65.7 ↑</td>
<td class="center">68.8</td>
<td class="center">64.7 ↑</td>
</tr>
<tr>
<td>w/o V</td>
<td class="center">--</td>
<td class="center">70.0 ↓</td>
<td class="center">58.8 ↑</td>
<td class="center">52.8 ↓</td>
<td class="center">44.1 ↓</td>
<td class="center">57.1 ↑</td>
<td class="center">63.2</td>
<td class="center">57.7</td>
</tr>
<tr>
<td>w/o T</td>
<td class="center">--</td>
<td class="center">73.7</td>
<td class="center">60.4 ↑</td>
<td class="center">56.4</td>
<td class="center">49.8 ↑</td>
<td class="center">44.2 ↓</td>
<td class="center">60.7 ↓</td>
<td class="center">57.5 ↓</td>
</tr>
</tbody>
<thead>
<tr>
<th>Modality</th>
<th>Neutral</th>
<th>Happy</th>
<th>Sad</th>
<th>Angry</th>
<th>Surprise</th>
<th>Fear</th>
<th>Disgust</th>
<th>UAR</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="9" class="center"><b>MELD</b></td>
</tr>
<tr>
<td>A+V+T</td>
<td class="center">68.7</td>
<td class="center">65.9</td>
<td class="center">53.8</td>
<td class="center">59.7</td>
<td class="center">69.0</td>
<td class="center">44.0</td>
<td class="center">42.6</td>
<td class="center">57.7</td>
</tr>
<tr>
<td colspan="9" class="center">Setup 1. Modality feature nullification</td>
</tr>
<tr>
<td>w/o A</td>
<td class="center">64.1</td>
<td class="center">67.4 ↑</td>
<td class="center">52.4</td>
<td class="center">48.7</td>
<td class="center">71.5 ↑</td>
<td class="center">40.0</td>
<td class="center">30.9 ↓</td>
<td class="center">53.6</td>
</tr>
<tr>
<td>w/o V</td>
<td class="center">58.2</td>
<td class="center">65.9</td>
<td class="center">59.1 ↑</td>
<td class="center">38.6 ↓</td>
<td class="center">70.5 ↑</td>
<td class="center">40.0</td>
<td class="center">35.3</td>
<td class="center">52.5</td>
</tr>
<tr>
<td>w/o T</td>
<td class="center">37.9 ↓</td>
<td class="center">2.7 ↓</td>
<td class="center">10.6 ↓</td>
<td class="center">84.9 ↑</td>
<td class="center">1.1 ↓</td>
<td class="center">20.0 ↓</td>
<td class="center">41.2</td>
<td class="center">28.3 ↓</td>
</tr>
<tr>
<td colspan="9" class="center">Setup 2. Modality exclusion</td>
</tr>
<tr>
<td>w/o A</td>
<td class="center">67.6</td>
<td class="center">64.9</td>
<td class="center">44.7</td>
<td class="center">49.9</td>
<td class="center">66.5</td>
<td class="center">32.0</td>
<td class="center">42.6</td>
<td class="center">52.6</td>
</tr>
<tr>
<td>w/o V</td>
<td class="center">66.9</td>
<td class="center">37.8 ↓</td>
<td class="center">43.3 ↓</td>
<td class="center">54.2</td>
<td class="center">49.8</td>
<td class="center">20.0</td>
<td class="center">25.0</td>
<td class="center">42.4</td>
</tr>
<tr>
<td>w/o T</td>
<td class="center">31.8 ↓</td>
<td class="center">43.3</td>
<td class="center">55.3 ↑</td>
<td class="center">46.4 ↓</td>
<td class="center">13.9 ↓</td>
<td class="center">16.0 ↓</td>
<td class="center">23.5 ↓</td>
<td class="center">32.9 ↓</td>
</tr>
</tbody>
<thead>
<tr>
<th>Modality</th>
<th>Neutral</th>
<th>Happy</th>
<th>Sad</th>
<th>Angry</th>
<th>Surprise</th>
<th>Fear</th>
<th>Disgust</th>
<th>UAR</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="9" class="center"><b>AFEW</b></td>
</tr>
<tr>
<td>A+V+T</td>
<td class="center">84.1</td>
<td class="center">88.7</td>
<td class="center">74.2</td>
<td class="center">70.3</td>
<td class="center">48.9</td>
<td class="center">60.9</td>
<td class="center">53.7</td>
<td class="center">68.7</td>
</tr>
<tr>
<td colspan="9" class="center">Setup 1. Modality feature nullification</td>
</tr>
<tr>
<td>w/o A</td>
<td class="center">76.2</td>
<td class="center">90.3 ↑</td>
<td class="center">53.2</td>
<td class="center">43.8 ↓</td>
<td class="center">51.1 ↑</td>
<td class="center">54.3</td>
<td class="center">46.3</td>
<td class="center">59.3</td>
</tr>
<tr>
<td>w/o V</td>
<td class="center">49.2 ↓</td>
<td class="center">40.3 ↓</td>
<td class="center">35.5 ↓</td>
<td class="center">54.7</td>
<td class="center">26.7 ↓</td>
<td class="center">50.0</td>
<td class="center">29.3 ↓</td>
<td class="center">40.8 ↓</td>
</tr>
<tr>
<td>w/o T</td>
<td class="center">60.3</td>
<td class="center">74.4</td>
<td class="center">75.8 ↑</td>
<td class="center">75.0 ↑</td>
<td class="center">42.2</td>
<td class="center">32.6 ↓</td>
<td class="center">34.1</td>
<td class="center">56.8</td>
</tr>
<tr>
<td colspan="9" class="center">Setup 2. Modality exclusion</td>
</tr>
<tr>
<td>w/o A</td>
<td class="center">68.3</td>
<td class="center">61.3</td>
<td class="center">41.9 ↓</td>
<td class="center">50.0 ↓</td>
<td class="center">53.3 ↑</td>
<td class="center">45.7</td>
<td class="center">51.2</td>
<td class="center">53.1</td>
</tr>
<tr>
<td>w/o V</td>
<td class="center">60.3 ↓</td>
<td class="center">37.1 ↓</td>
<td class="center">41.9 ↓</td>
<td class="center">70.3</td>
<td class="center">20.0 ↓</td>
<td class="center">43.5 ↓</td>
<td class="center">14.6 ↓</td>
<td class="center">41.1 ↓</td>
</tr>
<tr>
<td>w/o T</td>
<td class="center">69.8</td>
<td class="center">79.0</td>
<td class="center">74.2</td>
<td class="center">70.3</td>
<td class="center">62.2 ↑</td>
<td class="center">43.5 ↓</td>
<td class="center">43.9</td>
<td class="center">63.3</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
<div class="table-section">
<div class="container is-max-desktop">
<div class="table">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h6 class="title is-6">Multi-corpus experimental results (UAR, %) based on single-corpus trained models. Δ means difference between diagonal and average values</h6>
<table>
<thead>
<tr>
<th rowspan="2" class="rc">Training corpus</th>
<th colspan="4">Test subset</th>
<th rowspan="2" class="rc">Average</th>
</tr>
<tr>
<th>IEMOCAP</th>
<th>MOSEI</th>
<th>MELD</th>
<th>AFEW</th>
</tr>
</thead>
<tbody>
<tr>
<td>IEMOCAP</td>
<td class="center">71.3</td>
<td class="center">15.5</td>
<td class="center">22.9</td>
<td class="center">34.1</td>
<td class="center">24.7</td>
</tr>
<tr>
<td>MOSEI</td>
<td class="center">38.2</td>
<td class="center">38.5</td>
<td class="center">34.3</td>
<td class="center">44.3</td>
<td class="center">38.9</td>
</tr>
<tr>
<td>MELD</td>
<td class="center">42.3</td>
<td class="center">30.4</td>
<td class="center">62.0</td>
<td class="center">42.1</td>
<td class="center">38.3</td>
</tr>
<tr>
<td>AFEW</td>
<td class="center">47.3</td>
<td class="center">28.4</td>
<td class="center">33.3</td>
<td class="center">79.3</td>
<td class="center">37.6</td>
</tr>
<tr>
<td>Average</td>
<td class="center">42.6 (Δ 28.7)</td>
<td class="center">24.8 (Δ 13.7)</td>
<td class="center">30.2 (Δ 31.8)</td>
<td class="center">40.2 (Δ 39.1)</td>
<td class="center">—</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
<div class="table-section">
<div class="container is-max-desktop">
<div class="table">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h6 class="title is-6">Results (UAR, %) based on multi-corpus trained models and different encoders. Without corpus (e.g. w/o MELD) performance is for Test subsets of all training corpora, excluding MELD. ↓ shows the least generalization ability.</h6>
<table>
<thead>
<tr>
<th rowspan="2" class="rc">Encoder</th>
<th colspan="8">Test subset</th>
</tr>
<tr>
<th>W/o IEMOCAP</th>
<th>LOCO IEMOCAP</th>
<th>W/o MOSEI</th>
<th>LOCO MOSEI</th>
<th>W/o MELD</th>
<th>LOCO MELD</th>
<th>W/o AFEW</th>
<th>LOCO AFEW</th>
</tr>
</thead>
<tbody>
<tr>
<td>IEMOCAP</td>
<td class="center">--</td>
<td class="center">--</td>
<td class="center">59.7</td>
<td class="center">31.7</td>
<td class="center">49.6</td>
<td class="center">32.8</td>
<td class="center">50.7</td>
<td class="down">42.9 ↓</td>
</tr>
<tr>
<td>MOSEI</td>
<td class="down">43.0 ↓</td>
<td class="center">44.5</td>
<td class="center">--</td>
<td class="center">--</td>
<td class="down">43.2 ↓</td>
<td class="center">33.0</td>
<td class="center">49.4</td>
<td class="center">48.6</td>
</tr>
<tr>
<td>MELD</td>
<td class="center">50.1</td>
<td class="center">44.4</td>
<td class="center">59.9</td>
<td class="center">31.6</td>
<td class="center">--</td>
<td class="center">--</td>
<td class="center">53.1</td>
<td class="center">49.0</td>
</tr>
<tr>
<td>AFEW</td>
<td class="center">48.2</td>
<td class="center">44.1</td>
<td class="center">59.8</td>
<td class="down">28.5 ↓</td>
<td class="center">52.3</td>
<td class="center">32.2</td>
<td class="center">--</td>
<td class="center">--</td>
</tr>
<tr>
<td>Average</td>
<td class="center">47.1</td>
<td class="center">44.3</td>
<td class="center">59.8</td>
<td class="center">30.6</td>
<td class="center">48.4</td>
<td class="center">32.7</td>
<td class="center">51.1</td>
<td class="center">46.8</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
<div class="table-section">
<div class="container is-max-desktop">
<div class="table">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h6 class="title is-6">Comparison with existing MER methods. wF1 refers to weighted F1, mF1 to macro F1, WL to models trained with the weighted loss.</h6>
<table>
<thead>
<tr>
<th>Method</th>
<th>Year</th>
<th>Corpus</th>
<th>Modality</th>
<th>wAcc</th>
<th>wF1</th>
<th>Acc</th>
<th>F1</th>
</tr>
</thead>
<tbody>
<tr>
<td>Le et al.</td>
<td>2023</td>
<td rowspan="6" style="border-bottom:none;">MOSEI</td>
<td rowspan="6" style="border-bottom:none;">A+V+T</td>
<td>67.8</td>
<td>--</td>
<td>--</td>
<td>47.6</td>
</tr>
<tr>
<td>MAGDRA</td>
<td>2024</td>
<td>--</td>
<td>--</td>
<td>48.8</td>
<td>56.3</td>
</tr>
<tr>
<td>TAILOR</td>
<td>2022</td>
<td>--</td>
<td>--</td>
<td>48.8</td>
<td>56.9</td>
</tr>
<tr>
<td>CARAT</td>
<td>2024</td>
<td>66.4</td>
<td>78.8</td>
<td>49.4</td>
<td><strong>58.1</strong></td>
</tr>
<tr>
<td>Ours (w/o WL)</td>
<td>2024</td>
<td>61.4</td>
<td><strong>80.4</strong></td>
<td><strong>49.7</strong></td>
<td>54.0</td>
</tr>
<tr>
<td>Ours (WL)</td>
<td>2024</td>
<td><strong>69.3</strong></td>
<td>77.7</td>
<td>46.2</td>
<td>53.4</td>
</tr>
<thead>
<tr>
<th>Method</th>
<th>Year</th>
<th>Corpus</th>
<th>Modality</th>
<th>Acc</th>
<th>wF1</th>
<th>mF1</th>
<th>UAR</th>
</tr>
</thead>
<tr>
<td>TelME</td>
<td>2024</td>
<td rowspan="6">IEMOCAP</td>
<td rowspan="6">A+V+T</td>
<td>--</td>
<td>70.5</td>
<td>--</td>
<td>68.6</td>
</tr>
<tr>
<td>CORECT</td>
<td>2023</td>
<td>69.9</td>
<td>70.0</td>
<td>--</td>
<td>70.9</td>
</tr>
<tr>
<td>Yao et al.</td>
<td>2024</td>
<td>71.2</td>
<td>71.2</td>
<td>--</td>
<td>--</td>
</tr>
<tr>
<td>M³Net</td>
<td>2023</td>
<td>72.5</td>
<td>72.5</td>
<td>71.5</td>
<td>--</td>
</tr>
<tr>
<td>Ours (w/o WL)</td>
<td>2024</td>
<td>71.9</td>
<td>71.7</td>
<td>70.5</td>
<td>70.2</td>
</tr>
<tr>
<td>Ours (WL)</td>
<td>2024</td>
<td><strong>72.9</strong></td>
<td><strong>72.8</strong></td>
<td><strong>72.0</strong></td>
<td><strong>71.6</strong></td>