The Eloquence team submission for task 1 of MLC-SLM challenge
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908467268157440 |
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| author | Concina, Lorenzo Luque, Jordi Brutti, Alessio Matassoni, Marco Zhang, Yuchen |
| author_facet | Concina, Lorenzo Luque, Jordi Brutti, Alessio Matassoni, Marco Zhang, Yuchen |
| contents | 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 conversational speech recognition through the development of speech language models architectures. Given the increasing relevance of real-world conversational data for building robust Spoken Dialogue Systems, we explore three approaches to multilingual ASR. First, we conduct an evaluation of the official baseline to better understand its strengths and limitations, by training two projectors (linear and qformer) with different foundation models. Second we leverage the SLAM-ASR framework to train a custom multilingual linear projector. Finally we investigate the role of contrastive learning and the extended conversational context in enhancing the robustness of recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19308 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Eloquence team submission for task 1 of MLC-SLM challenge Concina, Lorenzo Luque, Jordi Brutti, Alessio Matassoni, Marco Zhang, Yuchen Sound Computation and Language Audio and Speech Processing 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 conversational speech recognition through the development of speech language models architectures. Given the increasing relevance of real-world conversational data for building robust Spoken Dialogue Systems, we explore three approaches to multilingual ASR. First, we conduct an evaluation of the official baseline to better understand its strengths and limitations, by training two projectors (linear and qformer) with different foundation models. Second we leverage the SLAM-ASR framework to train a custom multilingual linear projector. Finally we investigate the role of contrastive learning and the extended conversational context in enhancing the robustness of recognition. |
| title | The Eloquence team submission for task 1 of MLC-SLM challenge |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.19308 |