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Robust indoor speaker recognition in a network of audio and video sensors

机译：音频和视频传感器网络中的可靠室内说话人识别

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摘要

Situational awareness is achieved naturally by the human senses of sight and hearing in combination. Automatic scene understanding aims at replicating this human ability using microphones and cameras in cooperation. In this paper, audio and video signals are fused and integrated at different levels of semantic abstractions. We detect and track a speaker who is relatively unconstrained, i.e., free to move indoors within an area larger than the comparable reported work, which is usually limited to round table meetings. The system is relatively simple: consisting of just 4 microphone pairs and a single camera. Results show that the overall multimodal tracker is more reliable than single modality systems, tolerating large occlusions and cross-talk. System evaluation is performed on both single and multi-modality tracking. The performance improvement given by the audio-video integration and fusion is quantified in terms of tracking precision and accuracy as well as speaker diarisation error rate and precision-recall (recognition). Improvements vs. the closest works are evaluated: 56% sound source localisation computational cost over an audio only system, 8% speaker diarisation error rate over an audio only speaker recognition unit and 36% on the precision-recall metric over an audio-video dominant speaker recognition method.
机译：情景感知是通过人类视觉和听觉的组合自然而然地实现的。自动场景理解旨在协作使用麦克风和摄像头来复制这种人类能力。在本文中，音频和视频信号在语义抽象的不同级别被融合和集成。我们检测并跟踪相对不受限制的发言人，即可以在比可比的报告作品更大的区域内自由地在室内移动，该报告通常限于圆桌会议。该系统相对简单：仅包含4个麦克风对和一个摄像头。结果表明，整体多模式跟踪器比单模式系统更可靠，可以承受较大的遮挡和串扰。系统评估是在单模式和多模式跟踪上执行的。音频-视频集成和融合所带来的性能改进可通过跟踪精度和准确度以及说话者二值化错误率和精确调用（识别）来量化。评估与最接近的作品的改进：在仅音频的系统上，声源本地化计算成本为56％，在仅音频的扬声器识别单元上，说话者二分化错误率为8％，在音频视频主导系统上，精确召回率为36％说话人识别方法。

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