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Eloquence team submits multilingual ASR research for MLC-SLM challenge

By PulseAugur Editorial · [1 sources] ·

Researchers from the Eloquence team have submitted their work for the MLC-SLM challenge, focusing on multilingual conversational speech recognition. Their submission explores three distinct methods: evaluating the baseline model with different projectors, utilizing the SLAM-ASR framework for a custom multilingual projector, and investigating the impact of contrastive learning and extended conversational context on recognition robustness. The goal is to advance speech language model architectures for real-world conversational data. AI

IMPACT Advances research in multilingual conversational speech recognition, potentially improving dialogue systems.

RANK_REASON Submission of a research paper to a challenge/workshop. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Eloquence team submits multilingual ASR research for MLC-SLM challenge

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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Lorenzo Concina, Jordi Luque, Alessio Brutti, Marco Matassoni, Yuchen Zhang ·

    The Eloquence team submission for task 1 of MLC-SLM challenge

    arXiv:2507.19308v2 Announce Type: replace-cross Abstract: In this paper, we present our studies and experiments carried out for the task 1 of the Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM), which focuses on advancing multilingual convers…

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