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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="Audio-Visual Speech Recognition In-the-Wild: Multi-Angle Vehicle Cabin Dataset and Attention-Based Approach.">
<meta name="keywords" content="DAVIS, AVSR">
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<title>DAVIS</title>
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<h1 class="title is-1 publication-title">
Audio-Visual Speech Recognition In-the-Wild: Multi-Angle Vehicle Cabin Corpus and Attention-based Method
</h1>
<div class="is-size-5 publication-authors">
<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/3" target="_blank">Dmitry Ryumin</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">
Alexey Kashevnik<sup>2</sup>,
</span>
<span class="author-block">
<a href="https://hci.nw.ru/en/employees/1" target="_blank">Alexey Karpov</a><sup>1</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 />
<span><a href="https://2024.ieeeicassp.org/" target="_blank">ICASSP 2024</a> (accepted)</span>
</div>
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<span>Code</span>
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<span>Data</span>
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</div>
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</div>
</section>
<section class="section" style="padding: 0; margin:0">
<div class="TODO-section">
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<h2 class="title is-5">TODO List</h2>
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<svg viewBox="0 0 0 0" style="position: absolute; z-index: -1; opacity: 0;">
<defs>
<path id="todo__line" stroke="#363636" d="M21 12.3h280v0.1z" ></path>
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<div class="todo__text">Data collection</div>
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<div class="todo__text">Data labelling</div>
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<div class="todo__text">ICASSP 2024 paper submission</div>
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</svg>
<div class="todo__text">arXiv paper submission (coming soon)</div>
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</svg>
<div class="todo__text">Release code and models (coming soon) </div>
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</div>
</div>
<div class="abstract-section">
<div class="container is-max-desktop abstract-sect">
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
In recent years, audio-visual speech recognition (AVSR) gains increasing attention as an important part of human-machine interaction. However, the publicly available corpora are limited and lack in-the-wild recordings, especially in driving conditions when acoustic signal is frequently corrupted by background noise. Research so far has been collected in constrained environments, and thus cannot reflect the true performance of AVSR systems in real-world scenarios. Often there are no data available for languages other than English. To meet the request for research on AVSR in unconstrained driving conditions, this paper presents a corpus collected ‘in-the-wild’. Along with this, we propose cross-modal attention method for robust multi-angle AVSR for vehicle conditions that leverages visual context to improve both: recognition accuracy and noise robustness. We compare the impact of different state-of-the-art methods on the AVSR system. Our proposed model achieves state-of-the-art results on AVSR with 98.65% accuracy in recognising driver voice commands.
</p>
</div>
</div>
</div>
</div>
</div>
<div class="database-section">
<div class="container is-max-desktop">
<div class="database">
<h2 class="title is-3">In-car Audio-Visual Speech Corpus</h2>
<main class="sliders">
<div class="comp-container slider-first">
<div class="image-container">
<img class="image-before slider-image" src="./static/img/ex_1_1.png" alt="1">
<img class="image-after slider-image" src="./static/img/ex_1_2.png" alt="2">
</div>
<input type="range" min="0" max="100" value="50" class="slider slider-input-first" aria-label="Percentage of before photo shown"/>
<div class="slider-line"></div>
<div class="slider-button" aria-hidden="true">
<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" fill="#363636" viewBox="0 0 256 256"><path d="M136,40V216a8,8,0,0,1-16,0V40a8,8,0,0,1,16,0ZM96,120H35.31l18.35-18.34A8,8,0,0,0,42.34,90.34l-32,32a8,8,0,0,0,0,11.32l32,32a8,8,0,0,0,11.32-11.32L35.31,136H96a8,8,0,0,0,0-16Zm149.66,2.34-32-32a8,8,0,0,0-11.32,11.32L220.69,120H160a8,8,0,0,0,0,16h60.69l-18.35,18.34a8,8,0,0,0,11.32,11.32l32-32A8,8,0,0,0,245.66,122.34Z"></path></svg>
</div>
</div>
<div class="comp-container slider-second">
<div class="image-container">
<img class="image-before slider-image" src="./static/img/ex_2_1.png" alt="1">
<img class="image-after slider-image" src="./static/img/ex_2_2.png" alt="2">
</div>
<input type="range" min="0" max="100" value="50" class="slider slider-input-second" aria-label="Percentage of before photo shown"/>
<div class="slider-line"></div>
<div class="slider-button" aria-hidden="true">
<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" fill="#363636" viewBox="0 0 256 256"><path d="M136,40V216a8,8,0,0,1-16,0V40a8,8,0,0,1,16,0ZM96,120H35.31l18.35-18.34A8,8,0,0,0,42.34,90.34l-32,32a8,8,0,0,0,0,11.32l32,32a8,8,0,0,0,11.32-11.32L35.31,136H96a8,8,0,0,0,0-16Zm149.66,2.34-32-32a8,8,0,0,0-11.32,11.32L220.69,120H160a8,8,0,0,0,0,16h60.69l-18.35,18.34a8,8,0,0,0,11.32,11.32l32-32A8,8,0,0,0,245.66,122.34Z"></path></svg>
</div>
</div>
<div class="comp-container slider-third">
<div class="image-container">
<img class="image-before slider-image" src="./static/img/ex_3_1.png" alt="1">
<img class="image-after slider-image" src="./static/img/ex_3_2.png" alt="2">
</div>
<input type="range" min="0" max="100" value="50" class="slider slider-input-third" aria-label="Percentage of before photo shown"/>
<div class="slider-line"></div>
<div class="slider-button" aria-hidden="true">
<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" fill="#363636" viewBox="0 0 256 256"><path d="M136,40V216a8,8,0,0,1-16,0V40a8,8,0,0,1,16,0ZM96,120H35.31l18.35-18.34A8,8,0,0,0,42.34,90.34l-32,32a8,8,0,0,0,0,11.32l32,32a8,8,0,0,0,11.32-11.32L35.31,136H96a8,8,0,0,0,0-16Zm149.66,2.34-32-32a8,8,0,0,0-11.32,11.32L220.69,120H160a8,8,0,0,0,0,16h60.69l-18.35,18.34a8,8,0,0,0,11.32,11.32l32-32A8,8,0,0,0,245.66,122.34Z"></path></svg>
</div>
</div>
</main>
<div class="table-container">
<h2 class="title is-4">Corpus Parameters</h2>
<table class="table">
<thead>
<tr>
<th class="centered">Parameter</th>
<th class="centered">Value</th>
</tr>
</thead>
<tbody>
<tr>
<td>Number of speakers</td>
<td>20</td>
</tr>
<tr>
<td>Amount of voice commands</td>
<td>62</td>
</tr>
<tr>
<td>Command repetitions by each speaker</td>
<td>10</td>
</tr>
<tr>
<td>Number of annotated videos</td>
<td>≈ 22,350</td>
</tr>
<tr>
<td>Duration of audio-visual data</td>
<td>≈ 6 h 54 m</td>
</tr>
<tr>
<td>Clean speech percentage</td>
<td>95%</td>
</tr>
<tr>
<td>Video data format</td>
<td>mp4</td>
</tr>
<tr>
<td>Frame rate</td>
<td>60 FPS</td>
</tr>
<tr>
<td>Data volume</td>
<td>≈ 840 GB</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
<div class="method-section">
<div class="container is-max-desktop">
<div class="method">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Method Overview</h2>
<img class="img-method" src="./static/img/method.png" alt="method">
</div>
</div>
</div>
</div>
</div>
<div class="architectures-section">
<div class="container is-max-desktop">
<div class="architectures">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Neural Network Architectures </h2>
<img class="img-method" src="./static/img/architectures.png" alt="method">
</div>
</div>
</div>
</div>
</div>
<div class="results-section">
<div class="container is-max-desktop">
<div class="results">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Evaluation Results</h2>
</div>
</div>
<div class="table-container">
<table class="res-table">
<thead>
<tr>
<th rowspan="2" class="centered">#</th>
<th rowspan="2" class="centered second-th-col"> <p>Neural network model architecture</p></th>
<th colspan="2" class="centered">Accuracy, %</th>
</tr>
<tr>
<th class="centered">Val</th>
<th class="centered">Test</th>
</tr>
</thead>
<tbody>
<tr class="multicol">
<td colspan="4">VSR models</td>
</tr>
<tr>
<td class="centered">1</td>
<td>3DResNet18</td>
<td class="centered">86.53</td>
<td class="centered">76.74</td>
</tr>
<tr>
<td class="centered">2</td>
<td>3DResNet18 + SA</td>
<td class="centered">88.66</td>
<td class="centered">77.68</td>
</tr>
<tr>
<td class="centered">3</td>
<td>3DResNet18 + BiLSTM</td>
<td class="centered">82.94</td>
<td class="centered">75.89</td>
</tr>
<tr>
<td class="centered">4</td>
<td>3DResNet18 + SA + BiLSTM</td>
<td class="centered">85.55</td>
<td class="centered"><strong>83.46</strong></td>
</tr>
<tr class="multicol">
<td colspan="4">ASR models</td>
</tr>
<tr>
<td class="centered">5</td>
<td>2DResNet18</td>
<td class="centered">97.62</td>
<td class="centered">95.12</td>
</tr>
<tr>
<td class="centered">6</td>
<td>2DResNet18 + SA</td>
<td class="centered">97.90</td>
<td class="centered"><strong>95.61</strong></td>
</tr>
<tr class="multicol">
<td colspan="4">AVSR models</td>
</tr>
<tr>
<td class="centered">7</td>
<td>Concatenation-based fusion of 4 & 6</td>
<td class="centered">98.91</td>
<td class="centered">98.63</td>
</tr>
<tr>
<td class="centered">8</td>
<td>CMA-based fusion of 4 & 6</td>
<td class="centered">99.03</td>
<td class="centered"><strong>98.65</strong></td>
</tr>
</tbody>
</table>
</div>
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<h2 class="title is-3">Cite This Work</h2>
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<pre>
@inproceedings {
axyonov2024audio,
title={Audio-Visual Speech Recognition In-The-Wild: Multi-Angle Vehicle Cabin Corpus and Attention-Based Method},
author={Axyonov, Alexandr and Ryumin, Dmitry and Ivanko, Denis and Kashevnik, Alexey and Karpov, Alexey},
booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={8195--8199},
year={2024},
organization={IEEE},
doi={10.1109/icassp48485.2024.10448048}
}
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<li><a href="https://ieeexplore.ieee.org/abstract/document/9909819">Visual Speech Recognition in a Driver Assistance System</a></li>
<li><a href="https://www.isca-speech.org/archive/interspeech_2022/ivanko22_interspeech.html">DAVIS: Driver’s Audio-Visual Speech Recognition</a></li>
<li><a href="https://www.mdpi.com/1424-8220/23/4/2284">Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices</a></li>
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