META-CAT：通过元信息拼接实现说话人感知语音嵌入的多说话人自动语音识别
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR
AI总结：
提出端到端多说话人ASR框架Meta-Cat，通过拼接说话人监督信息掩蔽编码器激活，同时支持多说话人和目标说话人识别，性能优异且架构精简。
摘要：
我们提出了一种新颖的端到端多说话人自动语音识别（ASR）框架，该框架同时支持多说话人（MS）ASR和目标说话人（TS）ASR。我们的模型以完全端到端的方式训练，并整合了来自预训练说话人日志模块的说话人监督信息。我们引入了一种直观而有效的方法，即使用说话人监督模块的输出对ASR编码器激活进行掩蔽，我们将这一技术称为Meta-Cat（元信息拼接），该方法可同时应用于MS-ASR和TS-ASR。我们的结果表明，所提出的架构在MS-ASR和TS-ASR任务中均取得了具有竞争力的性能，且无需依赖传统方法，如神经掩蔽估计或在音频或特征层面进行掩蔽。此外，我们展示了一个统一双任务模型的雏形，该模型能够高效地同时处理MS-ASR和TS-ASR任务。因此，这项工作表明，可以通过精简的架构实现稳健的端到端多说话人ASR框架，从而无需采用先前研究中使用的复杂说话人过滤机制。
英文摘要：
We propose a novel end-to-end multi-talker automatic speech recognition (ASR) framework that enables both multi-speaker (MS) ASR and target-speaker (TS) ASR. Our proposed model is trained in a fully end-to-end manner, incorporating speaker supervision from a pre-trained speaker diarization module. We introduce an intuitive yet effective method for masking ASR encoder activations using output from the speaker supervision module, a technique we term Meta-Cat (meta-information concatenation), that can be applied to both MS-ASR and TS-ASR. Our results demonstrate that the proposed architecture achieves competitive performance in both MS-ASR and TS-ASR tasks, without the need for traditional methods, such as neural mask estimation or masking at the audio or feature level. Furthermore, we demonstrate a glimpse of a unified dual-task model which can efficiently handle both MS-ASR and TS-ASR tasks. Thus, this work illustrates that a robust end-to-end multi-talker ASR framework can be implemented with a streamlined architecture, obviating the need for the complex speaker filtering mechanisms employed in previous studies.
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。
