{"ID":22952658,"CreatedAt":"2026-09-17T02:12:05.498442134Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.18680","arxiv_id":"2609.18680","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.","short_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...","url_abs":"https://arxiv.org/abs/2609.18680","url_pdf":"https://arxiv.org/pdf/2609.18680v1","authors":"[\"Yifan Gao\",\"Yao Tian\",\"Hongbin Suo\"]","published":"2026-09-16T13:56:13Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.SD\"]","methods":"[]","has_code":false}