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Speaker Change Detection based on Mean Shift

机译:基于平均转移的扬声器改变检测

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—To settle out the problem that search of speaker change point (SCP) is blind and exhaustive, mean shift is proposed to seek SCP by estimating the kernel density of speech stream in this paper. It contains three steps: seeking peak points using mean shift firstly, using maximum likelihood ratio (MLR) to compute the MLR value of the peak points secondly, and seeking SCPs from MLR value using the maximum method thirdly. The relationship of MLR and BIC is given then. Compared with those methods of using metric or model, the process of seeking SCP is no longer blind because mean shift always points the direction of maximum increase in the density. The experiments show that the proposed algorithm can arrive a comparable result against to BIC and DISTBIC, while it can save detection time, for a 3-second speech segment , the time using the proposed algorithm is about 60% of DISTBIC and 45% of BIC . Further investigation and improvement about this method is discussed at the end of this paper.
机译:- 解决问题的问题,即寻找扬声器改变点(SCP)是盲目和详尽的,均建议通过估计本文中的语音流的内核密度来寻求SCP。它包含三个步骤:使用均值使用平均偏移的峰值点使用最大似然比(MLR)来计算峰值点的MLR值,并使用最大方法从第三种方法从MLR值寻找SCP。给出了MLR和BIC的关系。与使用度量或模型的方法相比,寻找SCP的过程不再是盲的,因为平均转移总是指向密度最大增加的方向。实验表明,该算法可以将可比结果与BIC和STATBIC到达,而它可以节省3秒语音段的检测时间,使用所提出的算法的时间约为STATTBIC和45%的BIC的60% 。在本文末尾讨论了对该方法的进一步调查和改进。

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