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            MULTI-PARTY MEETING TRANSCRIPTION CHALLENGE 2.0
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<li class="toctree-l2"><a class="reference internal" href="#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
<li class="toctree-l2"><a class="reference internal" href="#evaluation-metric">Evaluation metric</a></li>
<li class="toctree-l2"><a class="reference internal" href="#sub-track-arrangement">Sub-track arrangement</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#sub-track-i-fixed-training-condition">Sub-track I (Fixed Training Condition):</a></li>
<li class="toctree-l3"><a class="reference internal" href="#sub-track-ii-open-training-condition">Sub-track II (Open Training Condition):</a></li>
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  <div class="section" id="track-evaluation">
<h1>Track &amp; Evaluation<a class="headerlink" href="#track-evaluation" title="Permalink to this headline"></a></h1>
<div class="section" id="speaker-attributed-asr">
<h2>Speaker-Attributed ASR<a class="headerlink" href="#speaker-attributed-asr" title="Permalink to this headline"></a></h2>
<p>The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It’s worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps of the Test-2023 set. Instead, segments containing multiple speakers will be provided, which can be obtained using a simple voice activity detection (VAD) model.</p>
<p><img alt="task difference" src="_images/task_diff.png" /></p>
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<div class="section" id="evaluation-metric">
<h2>Evaluation metric<a class="headerlink" href="#evaluation-metric" title="Permalink to this headline"></a></h2>
<p>The accuracy of a speaker-attributed ASR system is evaluated using the concatenated minimum permutation character error rate (cpCER) metric. The calculation of cpCER involves three steps. Firstly, the reference and hypothesis transcriptions from each speaker in a session are concatenated in chronological order. Secondly, the character error rate (CER) is calculated between the concatenated reference and hypothesis transcriptions, and this process is repeated for all possible speaker permutations. Finally, the permutation with the lowest CER is selected as the cpCER for that session. TThe CER is obtained by dividing the total number of insertions (Ins), substitutions (Sub), and deletions(Del) of characters required to transform the ASR output into the reference transcript by the total number of characters in the reference transcript. Specifically, CER is calculated by:</p>
<div class="math notranslate nohighlight">
\[\text{CER} = \frac {\mathcal N_{\text{Ins}} + \mathcal N_{\text{Sub}} + \mathcal N_{\text{Del}} }{\mathcal N_{\text{Total}}} \times 100\%,\]</div>
<p>where <span class="math notranslate nohighlight">\(\mathcal N_{\text{Ins}}\)</span>, <span class="math notranslate nohighlight">\(\mathcal N_{\text{Sub}}\)</span>, <span class="math notranslate nohighlight">\(\mathcal N_{\text{Del}}\)</span> are the character number of the three errors, and <span class="math notranslate nohighlight">\(\mathcal N_{\text{Total}}\)</span> is the total number of characters.</p>
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<div class="section" id="sub-track-arrangement">
<h2>Sub-track arrangement<a class="headerlink" href="#sub-track-arrangement" title="Permalink to this headline"></a></h2>
<div class="section" id="sub-track-i-fixed-training-condition">
<h3>Sub-track I (Fixed Training Condition):<a class="headerlink" href="#sub-track-i-fixed-training-condition" title="Permalink to this headline"></a></h3>
<p>Participants are required to use only the fixed-constrained data (i.e., AliMeeting, Aishell-4, and CN-Celeb) for system development. The usage of any additional data is strictly prohibited. However, participants can use open-source pre-trained models from third-party sources, such as <a class="reference external" href="https://huggingface.co/models">Hugging Face</a> and <a class="reference external" href="https://www.modelscope.cn/models">ModelScope</a>, provided that the list of utilized models is clearly stated in the final system description paper.</p>
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<div class="section" id="sub-track-ii-open-training-condition">
<h3>Sub-track II (Open Training Condition):<a class="headerlink" href="#sub-track-ii-open-training-condition" title="Permalink to this headline"></a></h3>
<p>Participants are allowed to use any publicly available data set, privately recorded data, and manual simulation to build their systems in addition to the fixed-constrained data. They can also utilize any open-source pre-trained models, but it is mandatory to provide a clear list of the data and models used in the final system description paper. If manually simulated data is used, a detailed description of the data simulation scheme must be provided.</p>
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