HearInContext: A Benchmark for Implicit Context in Speech Recognition
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Computer Science > Computation and Language
Title:HearInContext: A Benchmark for Implicit Context in Speech Recognition
Abstract:Contextual ASR can benefit from semantic cues or from target words explicitly provided in the context. We introduce HearInContext, a Mandarin--English benchmark that pairs shared synthetic speech with assistant replies supporting different interpretations. The benchmark comprises 3,764 semantic test cases built around homophones. Implicit contexts exclude candidate words; explicit contexts name the target. No-context and unrelated-context controls measure the benefit of relevant history and sensitivity to irrelevant history. Context-capable models benefit from implicit cues but achieve higher target recall with explicit hints. Fine-tuning Qwen3-ASR-1.7B improves implicit-context target recall by 11.0 and 11.5 percentage points in Mandarin and English, respectively, while absolute CER/WER changes on AISHELL-1 and LibriSpeech remain below 0.1 percentage points. Gains extend to explicit conditions excluded from fine-tuning and to Mandarin hotword recognition on real recordings.
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2609.18680 [cs.CL] |
| (or arXiv:2609.18680v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.18680
arXiv-issued DOI via DataCite (pending registration)
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