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首页> 外文会议>Annual conference of the International Speech Communication Association;INTERSPEECH 2010 >Floor Holder Detection and End of Speaker Tlirn Prediction in Meetings
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Floor Holder Detection and End of Speaker Tlirn Prediction in Meetings

机译：会议中发言者的发言权预测和结束的发言者检测

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We propose a novel fully automatic framework to detect which meeting participant is currently holding the conversational floor and when the current speaker turn is going to finish. Two sets of experiments were conducted on a large collection of multiparty conversations: the AMI meeting corpus. Unsupervised speaker turn detection was performed by post-processing the speaker diarization and the speech activity detection outputs. A supervised end-of-speaker-turn prediction framework, based on Dynamic Bayesian Networks and automatically extracted mul-timodal features (related to prosody, overlapping speech, and visual motion), was also investigated. These novel approaches resulted in good floor holder detection rates (13.2% Floor Error Rate), attaining state of the art end-of-speaker-turn prediction performances.
机译：我们提出了一种新颖的全自动框架，用于检测哪个会议参与者当前正在主持会议，以及当前发言人的讲话何时结束。在大量多方对话中进行了两组实验：AMI会议语料库。通过对扬声器的二值化和语音活动检测输出进行后处理来执行无监督的扬声器转弯检测。还研究了基于动态贝叶斯网络并自动提取多模态特征（与韵律，重叠语音和视觉运动有关）的有监督的发言者转弯预测框架。这些新颖的方法产生了良好的落地支架检测率（13.2％地板错误率），达到了最先进的扬声器转弯预测性能。

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