A frozen Whisper-large-v3 encoder plus a trainable adaptor plus LoRA-adapted Qwen2.5-7B achieves 9.83% WER/CER on 11-language conversational ASR and third place in MLC-SLM 2025 Track 1.
Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge
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abstract
This paper presents the architecture and performance of a novel Multilingual Automatic Speech Recognition (ASR) system developed by the Transsion Speech Team for Track 1 of the MLC-SLM 2025 Challenge. The proposed system comprises three key components: 1) a frozen Whisper-large-v3 based speech encoder, leveraging large-scale pretraining to ensure robust acoustic feature extraction; 2) a trainable adaptor module using Linear-ReLU-Linear transformation mechanisms to effectively align speech and text representations; and 3) a frozen Qwen2.5-7B-Instruct large language model (LLM) integrated with trainable LoRA for optimized contextual linguistic decoding. By systematically combining pretrained models with task specific fine-tuning, the system achieved a word/character error rate (WER/CER) of 9.83% across 11 languages in the evaluation set and ranked third place among global participants.
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Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge
A frozen Whisper-large-v3 encoder plus a trainable adaptor plus LoRA-adapted Qwen2.5-7B achieves 9.83% WER/CER on 11-language conversational ASR and third place in MLC-SLM 2025 Track 1.