用于端到端多通道多说话人ASR的与数据无关的波束成形
Data-independent Beamforming for End-to-end Multichannel Multi-speaker ASR
AI总结：
本文提出一种与数据无关且无需训练的波束成形方法，通过处理特定角度扇区提升多通道多说话人ASR性能，在AMI语料库上词错误率最高降11%，说话人计数准确率最高升27%。
摘要：
由于环境噪声、混响和说话人重叠，多通道、多说话人场景中的自动语音识别（ASR）仍然具有挑战性。在本文中，我们提出了一种波束成形方法，该方法在应用端到端多通道、多说话人ASR系统之前，根据球极坐标处理特定的角度扇区。该方法与数据无关且无需训练。我们证明，与使用相同数量的原始麦克风信号相比，使用一组波束成形信号可以提高ASR性能。此外，增加用于波束成形的信号数量可进一步提高识别准确率，从而更有效地利用多通道信号，同时减少ASR系统的整体输入负载。我们在AMI会议语料库上进行了实验，与不利用波束成形的多通道ASR基线系统相比，所提出的方法将词错误率相对降低了高达11%，并将说话人计数准确率相对提高了高达27%。
英文摘要：
Automatic speech recognition (ASR) in multichannel, multi-speaker scenarios remains challenging due to ambient noise, reverberation and overlapping speakers. In this paper, we propose a beamforming approach that processes specific angular sectors based on their spherical polar coordinates before applying an end-to-end multichannel, multi-speaker ASR system. This method is data-independent and training-free. We demonstrate that using a group of beamformed signals improves ASR performance compared to using the same number of raw microphone signals. Moreover, increasing the number of signals used for beamforming further enhances recognition accuracy, leading to a more efficient use of multichannel signals while reducing the overall input load for the ASR system. We conduct experiments on the AMI meeting corpus, where the proposed method reduces word error rate by up to 11% and improves speaker counting accuracy by up to 27% relative compared to a multichannel ASR baseline system that does not exploit beamforming.
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。
