NTU团队用提示词与模型平均法，让多语言语音识别错误率降低近一半。
NTU Speechlab LLM-Based Multilingual ASR System for Interspeech MLC-SLM Challenge 2025
- 采用语言特定提示词和模型平均提升多语言识别性能
- 错误率从20.2%降至10.6%，相对下降48%
- 适合研究多语言语音大模型的开发者参考
本报告介绍了南洋理工语音实验室为Interspeech 2025多语言对话语音与语言模型挑战赛（任务I）开发的系统，最终取得第五名。我们对多语言自动语音识别系统进行了全面分析，重点展示了在模型架构、数据筛选和训练策略上的关键进展。特别是语言特定提示词和模型平均技术显著提升了跨多种语言的表现。相比初始基线系统，最终模型在评测集上将平均混合错误率从20.2%降至10.6%，绝对降低9.6%（相对改进48%）。结果验证了该方法的有效性，并为未来语音大模型研究提供了实用洞见。
原文摘要 · Abstract (English)
This report details the NTU Speechlab system developed for the Interspeech 2025 Multilingual Conversational Speech and Language Model (MLC-SLM) Challenge (Task I), where we achieved 5th place. We present comprehensive analyses of our multilingual automatic speech recognition system, highlighting key advancements in model architecture, data selection, and training strategies. In particular, language-specific prompts and model averaging techniques were instrumental in boosting system performance across diverse languages. Compared to the initial baseline system, our final model reduced the average Mix Error Rate from 20.2% to 10.6%, representing an absolute improvement of 9.6% (a relative improvement of 48%) on the evaluation set. Our results demonstrate the effectiveness of our approach and offer practical insights for future Speech Large Language Models.
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