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arXiv · 2606.28884

GigaSpeechBench: A Real-World Multilingual Speech-to-Text Benchmark

Abstract

While modern ASR systems achieve low error rates on high-resource benchmarks, such performance often overestimates real-world robustness. Existing evaluations address challenges in isolation, lacking a unified benchmark for domain terminology, age variation, dialects, accents, and low-resource languages, particularly across the Middle East and Southeast Asia, representing over one billion under-evaluated speakers. To address this gap, we introduce GigaSpeechBench, a comprehensive multilingual and multidimensional in-the-wild ASR & AST benchmark comprising 680 hours of human-annotated speech. It features five modules: (1) 12 low-resource Middle Eastern and Southeast Asian languages, plus challenging Japanese and Korean; (2) 6 Chinese dialects; (3) 6 English accents; (4) dense terminology across 12 vertical domains for Chinese and English; and (5) older adult and child speech. We further provide human-annotated Chinese and English translations for 11 languages to support AST evaluation. Extensive evaluations of leading foundation models and commercial APIs reveal significant performance degradation in these challenging settings, exposing critical evaluation blind spots.

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Yujie Tu, Yifan Yang, Tianrui Wang, Yanqiao Zhu, Guodong Lin, Mingchen Shao, Haoran Wang, Junzhe Liu, Yuxiang Fu, Yizhou Peng, Changsong Liu, Peng Wang, Zhikang Niu, Yunchong Xiao, Haolong Zheng, Xiuwen Zheng, Xulin Fan, Wei-Qiang Zhang, Lei Xie, Longbiao Wang, Eng-Siong Chng, Jiajun Zhang, Kele Xu, Jianwei Yu, Binbin Zhang, Jiayu Du, Wupeng Wang, Zhigao Chen, Yuzhong Wu, Zhendong Peng, Bin Ma, Guoguo Chen, Xipeng Qiu, Mark Hasegawa-Johnson, Kai Yu, Zhifu Gao, Xiangang Li, Xie Chen. 2026-07-21. GigaSpeechBench: A Real-World Multilingual Speech-to-Text Benchmark. https://arxiv.org/abs/2606.28884

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