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<!DOCTYPE html>
<html>
<head lang="en">
<meta charset="UTF-8">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<title>Bengali.AI Massively Crowdsourced OOD-Speech Dataset</title>
<meta name="description" content="">
<meta name="viewport" content="width=device-width, initial-scale=1">
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<meta property="og:url" content="https://jonbarron.info/zipnerf/"/>
<meta property="og:title" content="Bengali.AI Speech Recognition Project" />
<meta property="og:description" content="We present OOD-Speech, the first out-of-distribution
(OOD) benchmarking dataset for Bengali automatic
speech recognition (ASR). Being one of the most spo-
ken languages globally, Bengali portrays large diversity
in dialects and prosodic features, which demands ASR
frameworks to be robust towards distribution shifts. For
example, islamic religious sermons in Bengali are deliv-
ered with a tonality that is significantly different from
regular speech. Our training dataset is collected via mas-
sively online crowdsourcing campaigns which resulted in
1177.94 hours collected and curated from 22, 645 native
Bengali speakers from South Asia. Our test dataset com-
prises 23.03 hours of speech collected and manually anno-
tated from 17 different sources, e.g., Bengali TV drama,
Audiobook, Talk show, Online class, and Islamic sermons
to name a few. OOD-Speech is jointly the largest pub-
licly available speech dataset, as well as the first out-of-
distribution ASR benchmarking dataset for Bengali." />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:title" content="Bengali.AI Speech Recognition Project" />
<meta name="twitter:description" content="We present OOD-Speech, the first out-of-distribution
(OOD) benchmarking dataset for Bengali automatic
speech recognition (ASR). Being one of the most spo-
ken languages globally, Bengali portrays large diversity
in dialects and prosodic features, which demands ASR
frameworks to be robust towards distribution shifts. For
example, islamic religious sermons in Bengali are deliv-
ered with a tonality that is significantly different from
regular speech. Our training dataset is collected via mas-
sively online crowdsourcing campaigns which resulted in
1177.94 hours collected and curated from 22, 645 native
Bengali speakers from South Asia. Our test dataset com-
prises 23.03 hours of speech collected and manually anno-
tated from 17 different sources, e.g., Bengali TV drama,
Audiobook, Talk show, Online class, and Islamic sermons
to name a few. OOD-Speech is jointly the largest pub-
licly available speech dataset, as well as the first out-of-
distribution ASR benchmarking dataset for Bengali." />
<meta name="twitter:image" content="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/tsne_all_ppx40.png" />
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<script src="js/app.js"></script>
<script src="js/video_comparison.js"></script>
</head>
<body>
<div class="container" id="main">
<div class="row">
<h2 class="col-md-12 text-center">
<b>OOD-Speech</b>: A Large Bengali Speech Recognition Dataset for
Out-of-Distribution Benchmarking</br>
<small>
INTERSPEECH 2023
</small>
</h2>
</div>
<div class="row">
<div class="col-md-12 text-center">
<ul class="list-inline">
<li>
<a href="https://www.linkedin.com/in/souhardya-saha/">
Souhardya Saha
</a>
</li>
<li>
<a href="https://www.linkedin.com/in/fazle-rakib/">
Fazle Rakib
</a>
</li>
<li>
<a href="https://scholar.google.com/citations?user=JmTaGcYAAAAJ&hl=en">
Shihab Istiak
</a>
</li>
<li>
<a href="https://scholar.google.com/citations?user=2Un1c7QAAAAJ&hl=en">
Samiul Alam
</a>
</li>
<li>
<a href="https://mnansary.github.io/">
Nazmuddoha Ansary
</a>
</li>
<li>
<a href="https://www.linkedin.com/in/mobassir-hossen-916a83137/">
Syed Mobasshir
</a>
</li>
<li>
<a href="https://www.sust.edu/institutes/iict/faculty/10">
Sayma Sultana
</a>
</li>
<li>
<a href="https://www.linkedin.com/in/marsia-haque-meghla-321021207/">
Marsia Meghla
</a>
</li>
<li>
<a href="https://people.bengali.ai/farig/">
Farig Sadeq
</a>
</li>
<li>
<a href="https://appledora.github.io/#/">
Nazia Tasnim
</a>
</li>
<li>
<a href="https://www.linkedin.com/in/mamunur-rahaman-mamun/?originalSubdomain=bd">
Mamun Rahman
</a>
</li>
<li>
<a href="https://scholar.google.com/citations?user=W_okWy0AAAAJ&hl=en">
Tahsin Reasat
</a>
</li>
<li>
<a href="https://people.bengali.ai/sushmit/">
Asif Sushmit
</a>
</li>
<li>
<a href="https://imtiazhumayun.github.io/">
Ahmed Imtiaz Humayun
</a>
</li>
</br>Bengali.AI
</ul>
</div>
</div>
<div class="row">
<div class="text-center">
<ul class="nav nav-pills nav-justified">
<li>
<a href="https://bengaliai.github.io/">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/logoBengaliai.jpg" height="60px">
<h4><strong>Bengali.AI Home</strong></h4>
</a>
</li>
<li>
<a href="https://arxiv.org/abs/2305.09688">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/interpaper.png" height="60px">
<h4><strong>Interspeech Paper & Supplementary Materials</strong></h4>
</a>
</li>
<li>
<a href="https://arxiv.org/pdf/2206.14053.pdf">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/commpaper.png" height="60px">
<h4><strong>Commonvoice + Dlsprint Paper</strong></h4>
</a>
</li>
<li>
<a href="https://shobdo.bengali.ai/prompts">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/record.png" height="60px">
<h4><strong>Contribute Data</strong></h4>
</a>
</li>
<li>
<a href="https://commonvoice.mozilla.org/bn/datasets">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/download.png" height="60px">
<h4><strong>Download Dataset</strong></h4>
</a>
</li>
<li>
<a href="https://www.kaggle.com/c/dlsprint">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/kaggle.png" height="60px">
<h4><strong>DLSprint: Kaggle Community Competition</strong></h4>
</a>
</li>
<li>
<a href="https://www.kaggle.com/competitions/bengaliai-speech">
<image src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/kaggle.png" height="60px">
<h4><strong>Kaggle Featured Competition Ongoing!!!</strong></h4>
</a>
</li>
</ul>
</div>
</div>
<!-- <div class="row">
<div class="col-md-8 col-md-offset-2">
<video id="v0" width="100%" autoplay loop muted controls>
<source src="img/teaser.mp4" type="video/mp4" />
</video>
</div>
</div> -->
<div class="row">
<div class="col-md-8 col-md-offset-2">
<div class="image">
<img src="https://raw.githubusercontent.com/BengaliAI/bengaliai.github.io/main/images/tsne_all_ppx40.png" alt="Your Image", width = 400, height=320>
</div>
<div class="text">
<p>Domain shift in different domains of speech data.</p>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Abstract
</h3>
<p class="text-justify">
We present OOD-Speech, the first out-of-distribution
(OOD) benchmarking dataset for Bengali automatic
speech recognition (ASR). Being one of the most spo-
ken languages globally, Bengali portrays large diversity
in dialects and prosodic features, which demands ASR
frameworks to be robust towards distribution shifts. For
example, islamic religious sermons in Bengali are deliv-
ered with a tonality that is significantly different from
regular speech. Our training dataset is collected via mas-
sively online crowdsourcing campaigns which resulted in
1177.94 hours collected and curated from 22, 645 native
Bengali speakers from South Asia. Our test dataset com-
prises 23.03 hours of speech collected and manually anno-
tated from 17 different sources, e.g., Bengali TV drama,
Audiobook, Talk show, Online class, and Islamic sermons
to name a few. OOD-Speech is jointly the largest pub-
licly available speech dataset, as well as the first out-of-
distribution ASR benchmarking dataset for Bengali.
</p>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Project Details
</h3>
<p class="text-justify">
This is the largest publicly available speech recognition corpus for Bengali. The data crowdsourcing started at 21st Feb 2022 and still ongoing.
The data contributors come from all walks life and the dataset has representative samples from different demographic strata and domains (gender, regional accent, age, background noise etc).
The test data comprises of challanging samples from different types of scripted and spontaneous speech recordings.
A Kaggle community research competition based on an earlier version of this dataset won the Kaggle Community Competition Award for the month of July, 2022.
Currently a dedicated team is extending the dataset to include representative samples for the different major regional dialects of Bengali. More big news coming!! Stay tuned.
</p>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
(Still Ongoing) Speech Recog. Project Contributors
</h3>
<p class="text-justify">
Data Collection and Curation: Souhardya Saha, Fazle Rakib, Marsia Meghla, Imtiaz Prio, Rezuwan Hassan, Mayank, Siha Haque, Rezwana Sultana, Tahmid Hossain</p>
<p class="text-justify">
Web Platform: Mamunur Rahman, Manash Kumar Mandal, Shakirul Islam </p>
<p class="text-justify">
Modeling and Analysis: Tahsin Reasat, Nazia Tasnim, Istiak Shihab, Nazmuddoha Ansary, Syed Mobasshir Hossen, Shahrin Nakkhatra, Zaowad Rahabin, Arijit Mukherjee
<p class="text-justify">
Linguists: Tanveer Azmal, Sazia Mehnaz, Kanij Fatema</p>
</div>
</div>
<!-- <div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Video
</h3>
<div class="text-center">
<div style="position:relative;padding-top:56.25%;">
<iframe src="https://youtube.com/embed/xrrhynRzC8k" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe>
</div>
</div>
</div>
</div> -->
<br>
<!-- <div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
360° Video Flythroughs
</h3>
<div class="text-center">
<div style="position:relative;padding-top:56.25%;">
<iframe src="https://www.youtube.com/embed/videoseries?list=PLzPoYEE6Aw7Jzjek1uEIPnpcTDL3u8tb4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe>
<!-- <iframe src="https://youtube.com/embed/jbE2ri8xEZo" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe> -->
</div>
</div>
</div>
</div> -->
<br>
<!-- <div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
XY aliasing
</h3>
<div class="video-compare-container" id="xyaliasDiv">
<video class="video" id="xyalias" loop playsinline autoPlay muted src="img/xy_alias_swipe_crf27.mp4" onplay="resizeAndPlay(this)"></video>
<canvas height=0 class="videoMerge" id="xyaliasMerge"></canvas>
</div>
<p class="text-justify">
A naive baseline (left) combining mip-NeRF 360 and Instant NGP results in aliasing as the camera moves laterally. Our full method (right) produces prefiltered renderings that do not flicker or shimmer.
</p>
</div>
</div>
-->
<!-- <div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Z aliasing
</h3>
<video id="v0" width="100%" autoplay loop muted controls>
<source src="img/z_alias_pdf_labeled.m4v" type="video/mp4" />
</video>
<p class="text-justify">
The proposal network used for resampling points along rays in mip-NeRF 360 results in an artifact we refer to as <em>z-aliasing</em>, where foreground content alternately appears and disappears as the camera moves toward or away from scene content. Z-aliasing occurs when the initial set of samples from the proposal network is not dense enough and misses thin structures, such as the chair above. Missed content can not be recovered by later rounds of sampling, since no future samples will be placed at that location along the ray. Our improvements to proposal network supervision result in a prefiltered proposal output that preserves the foreground object for all frames in this sequence. The plots above depict samples along a ray for three rounds of resampling (blue, orange, and green lines), with the y axis showing rendering weight (how much each interval contributes to the final rendered color), as a normalized probability density.
</p>
</div>
</div> -->
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Citation 1
</h3>
<div class="form-group col-md-10 col-md-offset-1">
<textarea id="bibtex" class="form-control" readonly>
@misc{2305.09688,
Author = {Fazle Rabbi Rakib and Souhardya Saha Dip and Samiul Alam and Nazia Tasnim and Md. Istiak Hossain Shihab and Md. Nazmuddoha Ansary and Syed Mobassir Hossen and Marsia Haque Meghla and Mamunur Mamun and Farig Sadeque and Sayma Sultana Chowdhury and Tahsin Reasat and Asif Sushmit and Ahmed Imtiaz Humayun},
Title = {OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking},
Year = {2023},
Eprint = {arXiv:2305.09688},
}</textarea>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Citation 2
</h3>
<div class="form-group col-md-10 col-md-offset-1">
<textarea id="bibtex" class="form-control" readonly>
@article{alam2022bengali,
title={Bengali common voice speech dataset for automatic speech recognition},
author={Alam, Samiul and Sushmit, Asif and Abdullah, Zaowad and Nakkhatra, Shahrin and Ansary, MD and Hossen, Syed Mobassir and Mehnaz, Sazia Morshed and Reasat, Tahsin and Humayun, Ahmed Imtiaz},
journal={arXiv preprint arXiv:2206.14053},
year={2022}
}</textarea>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Acknowledgements
</h3>
<p class="text-justify">
We thank Arijit Mukherjee, Mayank and Mozilla Foundation. Also thanks to BUET, SUST, DIU, UIU, DU RME, A2I Bangladesh, AUST, NUST, Green U., BracU CSE for their massive support. And the real heroes are the 25,000 people who have contributed for the development of this dataset.
<br><br>
sdf
</p>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Contact
</h3>
<p class="text-justify">
<br><br>
.
</p>
</div>
</div>
</div>
</body>
</html>