背景与方法
Spoken medical question answering can fail when automatic speech recognition (ASR) corrupts a clinically important term before retrieval.
We present MedAstra, a framework that maps frozen WavLM speech representations through a lightweight adapter into a frozen E5 text embedding space.
This enables a dense retrieval path that accepts a waveform without a query transcript.
Retrieved passages and speech-linked textual metadata support answer generation with entity alternatives, diagnostic contrasts, relation heuristics, and bounded retrieval refinement.
ASR-less refers to the initial speech retrieval interface; lexical retrieval, string-based entity correction, and the text generator require textual inputs. The deterministic speech embedding does not constitute a calibrated model of acoustic uncertainty.
结果与局限
- Reported retrieval results reach 87.6 Recall@10 for the speech-only retriever and 90.4 for the hybrid configuration, compared with 81.0 for transcript-first dense retrieval.
- Medical QA and ablations provide descriptive system comparisons, with input-provenance and evaluation limitations stated explicitly.
- We distinguish controlled synthetic acoustic tests from real clinical speech, qualify component attribution, and specify the remaining comparisons needed to assess speech-only QA and clinical generalization.