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CL-MASR: A Continual Learning Benchmark for Multilingual ASR
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  • Luca Della Libera ,
  • Pooneh Mousavi ,
  • Salah Zaiem ,
  • Cem Subakan ,
  • Mirco Ravanelli
Luca Della Libera
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Pooneh Mousavi
Concordia University

Corresponding Author:[email protected]

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Salah Zaiem
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Cem Subakan
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Mirco Ravanelli
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This paper introduces Continual Learning for Multilingual ASR (CL-MASR), a benchmark for continual learning applied to multilingual ASR. CL-MASR offers a curated selection of medium/low-resource languages, a modular and flexible platform for executing and evaluating various CL methods on top of existing large-scale pretrained multilingual ASR models such as Whisper and AWavLM, and a standardized set of evaluation metrics.