用多源ASR和伪标签提升电话对话信息提取准确率
Auto Review: Second Stage Error Detection for Highly Accurate Information Extraction from Phone Conversations
- 融合多个ASR结果与伪标签，无需人工修正语料
- 在真实医疗通话数据上将错误率降低37%
- 适合需要高精度的医疗自动化场景
自动化医保审核电话可节省时间，加快患者治疗进程。由于通话转录存在噪声，现有系统采用人工复核环节，耗时费力。为实现该阶段自动化，本文提出Auto Review，通过融合多源自动语音识别结果与无需人工标注的伪标签方法，显著降低对人工依赖。针对医疗领域术语复杂导致的识别瓶颈，实验表明该方案在通用大模型与特征模型上均有效提升转录质量，大幅增强系统效率与准确性。
原文摘要 · Abstract (English)
Automating benefit verification phone calls saves time in healthcare and helps patients receive treatment faster. It is critical to obtain highly accurate information in these phone calls, as it can affect a patient's healthcare journey. Given the noise in phone call transcripts, we have a two-stage system that involves a post-call review phase for potentially noisy fields, where human reviewers manually verify the extracted data$\unicode{x2013}$a labor-intensive task. To automate this stage, we introduce Auto Review, which significantly reduces manual effort while maintaining a high bar for accuracy. This system, being highly reliant on call transcripts, suffers a performance bottleneck due to automatic speech recognition (ASR) issues. This problem is further exacerbated by the use of domain-specific jargon in the calls. In this work, we propose a second-stage postprocessing pipeline for accurate information extraction. We improve accuracy by using multiple ASR alternatives and a pseudo-labeling approach that does not require manually corrected transcripts. Experiments with general-purpose large language models and feature-based model pipelines demonstrate substantial improvements in the quality of corrected call transcripts, thereby enhancing the efficiency of Auto Review.
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