[2409.10788] Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models

<!DOCTYPE html> <html lang="en"> <head> <title>[2409.10788] Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models</title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20241206" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/browse_search.css" /> <script language="javascript" src="/static/browse/0.3.4/js/accordion.js" /></script> <link rel="canonical" href="https://arxiv.org/abs/2409.10788"/> <meta name="description" content="Abstract page for arXiv paper 2409.10788: Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models"><meta property="og:type" content="website" /> <meta property="og:site_name" content="arXiv.org" /> <meta property="og:title" content="Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models" /> <meta property="og:url" content="https://arxiv.org/abs/2409.10788v2" /> <meta property="og:image" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:secure_url" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:width" content="1200" /> <meta property="og:image:height" content="700" /> <meta property="og:image:alt" content="arXiv logo"/> <meta property="og:description" content="Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of unlabeled speech data and then used for a range of downstream tasks. These models use a masked prediction objective, where the model learns to predict information about masked input segments from the unmasked context. The choice of prediction targets in this framework impacts their performance on downstream tasks. For instance, models pre-trained with targets that capture prosody learn representations suited for speaker-related tasks, while those pre-trained with targets that capture phonetics learn representations suited for content-related tasks. Moreover, prediction targets can differ in the level of detail they capture. Models pre-trained with targets that encode fine-grained acoustic features perform better on tasks like denoising, while those pre-trained with targets focused on higher-level abstractions are more effective for content-related tasks. Despite the importance of prediction targets, the design choices that affect them have not been thoroughly studied. This work explores the design choices and their impact on downstream task performance. Our results indicate that the commonly used design choices for HuBERT can be suboptimal. We propose approaches to create more informative prediction targets and demonstrate their effectiveness through improvements across various downstream tasks."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Exploring Prediction Targets in Masked Pre-Training for Speech..."/> <meta name="twitter:description" content="Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of unlabeled speech data and then used for a range of downstream tasks. These models use a masked..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models" /><meta name="citation_author" content="Chen, Li-Wei" /><meta name="citation_author" content="Higuchi, Takuya" /><meta name="citation_author" content="Bai, He" /><meta name="citation_author" content="Abdelaziz, Ahmed Hussen" /><meta name="citation_author" content="Rudnicky, Alexander" /><meta name="citation_author" content="Watanabe, Shinji" /><meta name="citation_author" content="Likhomanenko, Tatiana" /><meta name="citation_author" content="Theobald, Barry-John" /><meta name="citation_author" content="Aldeneh, Zakaria" /><meta name="citation_date" content="2024/09/16" /><meta name="citation_online_date" content="2025/01/17" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2409.10788" /><meta name="citation_arxiv_id" content="2409.10788" /><meta name="citation_abstract" content="Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of unlabeled speech data and then used for a range of downstream tasks. These models use a masked prediction objective, where the model learns to predict information about masked input segments from the unmasked context. The choice of prediction targets in this framework impacts their performance on downstream tasks. For instance, models pre-trained with targets that capture prosody learn representations suited for speaker-related tasks, while those pre-trained with targets that capture phonetics learn representations suited for content-related tasks. Moreover, prediction targets can differ in the level of detail they capture. Models pre-trained with targets that encode fine-grained acoustic features perform better on tasks like denoising, while those pre-trained with targets focused on higher-level abstractions are more effective for content-related tasks. Despite the importance of prediction targets, the design choices that affect them have not been thoroughly studied. This work explores the design choices and their impact on downstream task performance. Our results indicate that the commonly used design choices for HuBERT can be suboptimal. We propose approaches to create more informative prediction targets and demonstrate their effectiveness through improvements across various downstream tasks." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" tabindex="-1" href="/IgnoreMe"></a> 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Models</h1> <div class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/eess?searchtype=author&query=Chen,+L" rel="nofollow">Li-Wei Chen</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Higuchi,+T" rel="nofollow">Takuya Higuchi</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Bai,+H" rel="nofollow">He Bai</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Abdelaziz,+A+H" rel="nofollow">Ahmed Hussen Abdelaziz</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Rudnicky,+A" rel="nofollow">Alexander Rudnicky</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Watanabe,+S" rel="nofollow">Shinji Watanabe</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Likhomanenko,+T" rel="nofollow">Tatiana Likhomanenko</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Theobald,+B" rel="nofollow">Barry-John Theobald</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Aldeneh,+Z" rel="nofollow">Zakaria Aldeneh</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models, by Li-Wei Chen and 8 other authors</div> <a class="mobile-submission-download" href="/pdf/2409.10788">View PDF</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of unlabeled speech data and then used for a range of downstream tasks. These models use a masked prediction objective, where the model learns to predict information about masked input segments from the unmasked context. The choice of prediction targets in this framework impacts their performance on downstream tasks. For instance, models pre-trained with targets that capture prosody learn representations suited for speaker-related tasks, while those pre-trained with targets that capture phonetics learn representations suited for content-related tasks. Moreover, prediction targets can differ in the level of detail they capture. Models pre-trained with targets that encode fine-grained acoustic features perform better on tasks like denoising, while those pre-trained with targets focused on higher-level abstractions are more effective for content-related tasks. Despite the importance of prediction targets, the design choices that affect them have not been thoroughly studied. This work explores the design choices and their impact on downstream task performance. Our results indicate that the commonly used design choices for HuBERT can be suboptimal. We propose approaches to create more informative prediction targets and demonstrate their effectiveness through improvements across various downstream tasks. </blockquote>  <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax">ICASSP 2025</td> </tr> <tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Audio and Speech Processing (eess.AS)</span>; Sound (cs.SD)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2409.10788">arXiv:2409.10788</a> [eess.AS]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2409.10788v2">arXiv:2409.10788v2</a> [eess.AS]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> 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class="submission-history"> <h2>Submission history</h2> From: Zakaria Aldeneh [<a href="/show-email/477b763f/2409.10788" rel="nofollow">view email</a>] <br/> <strong><a href="/abs/2409.10788v1" rel="nofollow">[v1]</a></strong> Mon, 16 Sep 2024 23:51:42 UTC (2,616 KB)<br/> <strong>[v2]</strong> Fri, 17 Jan 2025 23:30:00 UTC (2,874 KB)<br/> </div> </div>  <div class="extra-services"> <div class="full-text"> <a name="other"></a> <span class="descriptor">Full-text links:</span> <h2>Access Paper:</h2> <ul> <div id="download-button-info" hidden> View a PDF of the paper titled Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models, by Li-Wei Chen and 8 other authors</div><li><a href="/pdf/2409.10788" aria-describedby="download-button-info" accesskey="f" class="abs-button download-pdf">View PDF</a></li><li><a href="/src/2409.10788" class="abs-button download-eprint">TeX Source</a></li><li><a href="/format/2409.10788" class="abs-button download-format">Other 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[2409.10788] Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models