LOTUSDIS：一个用于鲁棒对话式自动语音识别的泰语远场会议语料库
LOTUSDIS: A Thai far-field meeting corpus for robust conversational ASR
AI总结：
本文提出泰语远场会议语料库 LOTUSDIS，包含 114 小时多距离多设备自发对话，通过微调 Whisper 显著降低远场词错误率，证明距离多样化数据对鲁棒 ASR 的重要性。
摘要：
我们提出了 LOTUSDIS，一个公开可用的泰语会议语料库，旨在推进远场对话式自动语音识别（ASR）的发展。该数据集包含 114 小时的自发、无脚本对话，以 15-20 分钟的会话形式采集，每场有三名参与者，其中重叠语音频繁且自然。语音由九台独立的单通道设备同时录制，涵盖六种麦克风类型，距离从 0.12 米到 10 米不等，在不依赖麦克风阵列的情况下保留了混响、噪声和设备音染的真实效果。我们提供了标准的训练集、开发集、测试集划分，并发布了一个可复现的基线系统。我们在零样本和微调条件下对多个 Whisper 变体进行了基准测试。开箱即用的模型随距离增加表现出明显的性能下降，证实了预训练数据与泰语远场语音之间的不匹配。在 LOTUSDIS 上进行微调显著提升了鲁棒性：泰语 Whisper 基线将总体词错误率（WER）从 64.3 降至 38.3，将远场词错误率从 81.6 降至 49.5，在最远距离的麦克风上增益尤为显著。这些结果强调了距离多样化训练数据对于鲁棒 ASR 的重要性。该语料库以 CC-BY-SA 4.0 许可发布。我们还发布了训练和评估脚本作为基线系统，以促进该领域的可复现研究。
英文摘要：
We present LOTUSDIS, a publicly available Thai meeting corpus designed to advance far-field conversational ASR. The dataset comprises 114 hours of spontaneous, unscripted dialogue collected in 15-20 minute sessions with three participants, where overlapping speech is frequent and natural. Speech was recorded simultaneously by nine independent single-channel devices spanning six microphone types at distances from 0.12 m to 10 m, preserving the authentic effects of reverberation, noise, and device coloration without relying on microphone arrays. We provide standard train, dev, test splits and release a reproducible baseline system. We benchmarked several Whisper variants under zero-shot and fine-tuned conditions. Off-the-shelf models showed strong degradation with distance, confirming a mismatch between pre-training data and Thai far-field speech. Fine-tuning on LOTUSDIS dramatically improved robustness: a Thai Whisper baseline reduced overall WER from 64.3 to 38.3 and far-field WER from 81.6 to 49.5, with especially large gains on the most distant microphones. These results underscore the importance of distance-diverse training data for robust ASR. The corpus is available under CC-BY-SA 4.0. We also release training and evaluation scripts as a baseline system to promote reproducible research in this field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
