Past studies on end-to-end meeting transcription have focused on model architecture and have mostly been evaluated on simulated meeting data. We present a novel study aiming to optimize the use of a Speaker-Attributed ASR (SA-ASR) system in real-life scenarios, such as the AMI meeting corpus, for improved speaker assignment of speech segments. First, we propose a pipeline tailored to real-life applications involving Voice Activity Detection (VAD), Speaker Diarization (SD), and SA-ASR. Second, we advocate using VAD output segments to fine-tune the SA-ASR model, considering that it is also applied to VAD segments during test, and show that this results in a relative reduction of Speaker Error Rate (SER) up to 28%. Finally, we explore strategies to enhance the extraction of the speaker embedding templates used as inputs by the SA-ASR system. We show that extracting them from SD output rather than annotated speaker segments results in a relative SER reduction up to 20%.

翻译：以往关于端到端会议转录的研究主要聚焦于模型架构，且大多在模拟会议数据上进行评估。本文提出了一项新颖研究，旨在优化说话人属性语音识别（SA-ASR）系统在真实场景（如AMI会议语料库）中的使用，从而改进语音片段的说话人分配。首先，我们设计了一个针对真实应用场景的流水线，涵盖语音活动检测（VAD）、说话人日志（SD）和SA-ASR。其次，鉴于测试时系统同样应用于VAD输出片段，我们主张使用VAD输出片段对SA-ASR模型进行微调，实验表明该方法可使说话人错误率（SER）相对降低高达28%。最后，我们探索了增强SA-ASR系统输入说话人嵌入模板提取的策略。实验表明，从SD输出而非人工标注的说话人片段中提取嵌入模板，可使SER相对降低20%。