三X：一种基于LLM的多语言语音识别系统，用于INTERSPEECH2025 MLC-SLM挑战
Triple X: A LLM-Based Multilingual Speech Recognition System for the INTERSPEECH2025 MLC-SLM Challenge
AI总结：
本文提出Triple X系统，通过创新的编码器-适配器-LLM架构优化多语言对话场景的语音识别准确率，采用多阶段训练策略提升多语言识别性能，实验结果显示在开发和测试集上取得竞争力的词错误率表现。
摘要：
本文描述了我们提交给多语言对话语音语言建模（MLC-SLM）挑战任务1的Triple X语音识别系统。我们的工作聚焦于通过创新的编码器-适配器-LLM架构优化多语言对话场景的语音识别准确率。该框架利用文本基础大语言模型的强大推理能力，同时结合领域特定的适应。为进一步提升多语言识别性能，我们采用了精心设计的多阶段训练策略，利用大量多语言音频数据集。实验结果表明，我们的方法在开发和测试集上均取得了竞争力的词错误率（WER）表现，在挑战排名中获得第二名。
英文摘要：
This paper describes our Triple X speech recognition system submitted to Task 1 of the Multi-Lingual Conversational Speech Language Modeling (MLC-SLM) Challenge. Our work focuses on optimizing speech recognition accuracy in multilingual conversational scenarios through an innovative encoder-adapter-LLM architecture. This framework harnesses the powerful reasoning capabilities of text-based large language models while incorporating domain-specific adaptations. To further enhance multilingual recognition performance, we adopted a meticulously designed multi-stage training strategy leveraging extensive multilingual audio datasets. Experimental results demonstrate that our approach achieves competitive Word Error Rate (WER) performance on both dev and test sets, obtaining second place in the challenge ranking.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
