LibriConvo：从阅读文献模拟对话用于ASR和说话人日志
LibriConvo: Simulating Conversations from Read Literature for ASR and Diarization
AI总结：
提出LibriConvo合成对话语音语料库，基于说话人感知模拟对话框架构建，用于说话人日志和ASR基准测试，包含240.1小时音频，基线实验显示Sortformer在日志中优于pyannote，Fast Conformer-CTC在ASR中优于Whisper。
摘要：
我们介绍了LibriConvo，一个用于说话人日志和自动语音识别（ASR）的合成对话语音语料库，通过在数据集和基准测试设置中实例化先前提出的说话人感知模拟对话（SASC）框架构建而成。本文的主要贡献是基于该框架的语料库构建流程和基准测试。为了使数据更适合下游ASR和说话人日志，我们使用外部语音活动检测从英语CallHome估计对话时间统计信息，压缩长停顿，按书籍分组LibriTTS话语以改善局部语义连续性，并通过空间合理性启发式选择房间脉冲响应。生成的语料库包含240.1小时的音频，涉及830个说话人的1496个对话，划分为说话人不重叠的训练、验证和测试集。我们报告了说话人日志和ASR的基线结果。在测试集上，Sortformer在说话人日志中优于pyannote流水线（DER 11.1%对比24.4%）。对于ASR，使用序列化输出训练微调的Fast Conformer-CTC XLarge模型实现了7.29%的WER和6.97%的cpWER，优于零样本Whisper-large-v3。这些结果使LibriConvo成为研究合成对话语音和评估多说话人语音处理系统的实用基准。
英文摘要：
We introduce LibriConvo, a synthetic conversational speech corpus for speaker diarization and automatic speech recognition (ASR), built by instantiating the previously proposed Speaker-Aware Simulated Conversation (SASC) framework in a dataset and benchmarking setting. The main contribution of this paper is a corpus construction pipeline and benchmark derived from that framework. To make the data more suitable for downstream ASR and diarization, conversational timing statistics are estimated from English CallHome using external voice activity detection, long pauses are compressed, LibriTTS utterances are grouped by book to improve local semantic continuity, and room impulse responses are selected with a spatial-plausibility heuristic. The resulting corpus contains 240.1 hours of audio across 1,496 dialogues involving 830 speakers, partitioned into speaker-disjoint train, validation, and test splits. We report baseline results for both diarization and ASR. On the test split, Sortformer outperforms the pyannote pipeline in diarization (11.1\% vs.~24.4\% DER). For ASR, a Fast Conformer-CTC XLarge model fine-tuned with Serialized Output Training achieves 7.29\% WER and 6.97\% cpWER, outperforming zero-shot Whisper-large-v3. These results position LibriConvo as a practical benchmark for studying synthetic conversational speech and for evaluating multi-speaker speech processing systems.
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。
