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BUT System for the MLC-SLM Challenge

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0199410" target="_blank" >RIV/00216305:26230/26:0199410 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.isca-archive.org/mlcslm_2025/polok25_mlcslm.pdf" target="_blank" >https://www.isca-archive.org/mlcslm_2025/polok25_mlcslm.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.21437/mlcslm.2025-6" target="_blank" >10.21437/mlcslm.2025-6</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    BUT System for the MLC-SLM Challenge

  • Original language description

    We present a two-speaker automatic speech recognition (ASR) system that combines DiCoW—a diarization-conditioned variant of Whisper—with DiariZen, a diarization pipeline built on top of Pyannote. We first evaluate both systems in out-of-domain (OOD) multilingual scenarios without any fine-tuning. In this scenario, DiariZen consistently outperforms the baseline Pyannote diarization model, demonstrating strong generalization. Despite being fine-tuned on English-only data for target-speaker ASR, DiCoW retains solid multilingual performance,indicating that encoder modifications preserve Whisper’s multilingual capabilities. We then fine-tune both DiCoW and DiariZen on the MLC-SLM challenge data. The fine-tuned DiariZen continues to outperform the fine-tuned Pyannote baseline, while DiCoW sees further gains from domain adaptation. Our final system achieves a micro-average tcpWER/CER of 16.75 % and ranks second in Task 2 of the MLC-SLM challenge. Lastly, we identify several labeling inconsistencies in the training data—such as missing speech segments and incorrect silence annotations—which can hinder diarization fine-tuning. We propose simple mitigation strategies to address these issues and improve system robustness.

  • Czech name

  • Czech description

Classification

  • Type

    O - Miscellaneous

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2025

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů