mu-bench: A Multilingual Utterance Transcription Benchmark

Abstract:Voice agents depend on accurate automatic speech recognition (ASR) to act on what callers say, yet ASR is evaluated on read, English-centric speech with word error rate (WER), which penalizes surface rather than semantic differences. We introduce mu-bench, a dataset of 4,270 caller utterances from 250 phone calls to an AI banking agent in English, Spanish, Turkish, Vietnamese, and Mandarin, centered on form-field inputs such as names, email addresses, and confirmation codes. We release Utterance Error Rate (UER), an LLM judge of whether a transcript preserves meaning, calibrated against human raters, together with an LLM normalizer that makes WER comparable across providers' output formats. On 1,847 human-rated transcripts, UER agrees with annotators at $\kappa$ = 0.78, versus 0.53 for exact-match WER on normalized text. We rank six commercial providers on a public leaderboard; the best reaches 11.9% UER, and Mandarin is hardest for all six.

5 pages, 7 tables. Dataset: this https URL . Code: this https URL . Leaderboard: this https URL

Computation and Language (cs.CL)

arXiv:2609.32082 [cs.CL]

(or arXiv:2609.32082v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.32082 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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