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    content="Sidon is a fast, open-source multilingual speech restoration model for large-scale dataset restoration in TTS and spoken language modeling.">
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  <title>Sidon: Fast and Robust Open-Source Multilingual Speech Restoration</title>

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            <h1 class="title is-1 publication-title">Sidon: Fast and Robust Open-Source Multilingual Speech Restoration
              for Dataset Cleansing</h1>
            <div class="is-size-5 publication-authors">
              <span class="author-block">Wataru Nakata,</span>
              <span class="author-block">Yuki Saito,</span>
              <span class="author-block">Yota Ueda,</span>
              <span class="author-block">Hiroshi Saruwatari</span>
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            <div class="is-size-5 publication-authors">
              <span class="author-block">The University of Tokyo, Japan.</span>
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                  <a href="https://arxiv.org/abs/2509.17052" target="_blank" class="external-link button is-normal is-rounded is-dark">
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          <h2 class="title is-3">Abstract</h2>
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            <p>
              Large-scale text-to-speech (TTS) systems are limited by the scarcity of clean,
              multilingual recordings. We introduce <b>Sidon</b>, a fast, open-source
              speech restoration model that converts noisy in-the-wild speech into
              studio-quality speech and scales to dozens of languages. Sidon consists of
              two models: w2v-BERT 2.0 finetuned feature predictor to cleanse features from noisy speech
              and vocoder trained to synthesize restored speech from the cleansed features.
              Sidon achieves restoration performance comparable to Miipher: Google's internal speech restoration model with
              the aim of dataset cleansing for speech synthesis. Sidon is also computationally
              efficient, running up to 500× faster than real time on a
              single GPU. We further show that training a TTS model using a Sidon-cleansed automatic speech
              recognition corpus improves the quality of synthetic
              speech in a zero-shot setting. Code and model are released to
              facilitate reproducible dataset cleansing for the research community.
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      <h2 class="title is-3 has-text-centered">Full Multilingual Results (FLEURS)</h2>
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        <p>The full multilingual evaluation table is large. It is hidden by default.</p>
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      <details id="fleurs-details" class="box">
        <summary class="is-size-5">Show results table</summary>
        <p class="is-size-7 has-text-grey">Loads an embedded page with all language-wise metrics.</p>
        <div id="fleurs-iframe-wrapper" style="margin-top: 0.75rem;">
          <iframe title="FLEURS Results"
                  src="full_result.html"
                  loading="lazy"
                  style="width: 100%; height: 70vh; border: 1px solid #e5e5e5; border-radius: 6px;"></iframe>
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      <h2 class="title is-3 has-text-centered">Multilingual Samples from FLEURS</h2>
      <div id="samples-multilingual-root" class="samples-root"></div>

      <h2 class="title is-3 has-text-centered" style="margin-top:2rem;">English Demo Samples from LibriTTS</h2>
      <div id="samples-english-root" class="samples-root"></div>
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  <section class="section" id="BibTeX">
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      <h2 class="title">BibTeX</h2>
      <pre><code>@inproceedings{sidon2026,
  author    = {Nakata, Wataru and Saito, Yuki and Ueda, Yota and Saruwatari, Hiroshi},
  title     = {Sidon: Fast and Robust Open-Source Multilingual Speech Restoration for Dataset Restoration},
  booktitle = {TBA},
  year      = {TBA}
}</code></pre>
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