A Unified and Reproducible Experimentation Framework for Speech Understanding
Authors: Jing Peng, Junhao Du, Chenghao Wang, Hanqi Li, Yi Yang, Yixuan Wang, Xiaoyu Gu, Guanyu Chen, +16 more
Organizations: 1X-LANCE Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University · 2AISpeech Ltd, Suzhou, China · 3ETH Zürich, Switzerland · 5Hangzhou Dianzi University, Hangzhou, China · 6The Chinese University of Hong Kong, Shenzhen, China · 4Nanjing University, Suzhou, China
Speech foundation models and Speech LLMs have advanced speech understanding, yet deployment-oriented model selection is hindered by non-comparable evaluations caused by mismatched post-processing, and by training results that are hard to reproduce across data scales and pipelines. We present SURE, a unified experimentation framework that standardizes prediction formats, normalization, and scoring. SURE evaluates strong systems across paradigms, from conventional pipelines to Speech LLMs, on representative tasks under realistic acoustic and linguistic stressors. Beyond evaluation, SURE introduces an agent-assisted training conversion flow that maps paper and code into versioned, runnable training pipelines under a unified protocol on matched open-data subsets. Overall, SURE improves comparability and reproducibility for deployment-oriented evaluation.