端到端多通道说话人归属自动语音识别：说话人引导解码器与输入特征分析
End-to-end Multichannel Speaker-Attributed ASR: Speaker Guided Decoder and Input Feature Analysis
AI总结：
本文提出一种端到端多通道说话人归属ASR系统，结合Conformer编码器与说话人引导解码器，在模拟和真实数据上显著降低词错误率，并分析了输入特征的影响。
摘要：
我们提出了一种端到端多通道说话人归属自动语音识别（MC-SA-ASR）系统，该系统将基于Conformer的编码器与多帧跨通道注意力机制以及基于Transformer的说话人归属解码器相结合。据我们所知，这是首个在多通道设置中高效整合ASR与说话人识别模块的模型。在LibriSpeech数据的模拟混合语音上，与先前提出的单通道和多通道方法相比，我们的系统分别将词错误率（WER）相对降低了最多12%和16%。此外，我们研究了不同输入特征（包括多通道幅度和相位信息）对ASR性能的影响。最后，我们在AMI语料库上的实验证实了该系统在真实世界多通道会议转写中的有效性。
英文摘要：
We present an end-to-end multichannel speaker-attributed automatic speech recognition (MC-SA-ASR) system that combines a Conformer-based encoder with multi-frame crosschannel attention and a speaker-attributed Transformer-based decoder. To the best of our knowledge, this is the first model that efficiently integrates ASR and speaker identification modules in a multichannel setting. On simulated mixtures of LibriSpeech data, our system reduces the word error rate (WER) by up to 12% and 16% relative compared to previously proposed single-channel and multichannel approaches, respectively. Furthermore, we investigate the impact of different input features, including multichannel magnitude and phase information, on the ASR performance. Finally, our experiments on the AMI corpus confirm the effectiveness of our system for real-world multichannel meeting transcription.
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。
