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Cochannel Speaker Identification in Anechoic and Reverberant Conditions

机译:无回声和混响条件下的同频道发言人识别

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摘要

Speaker identification (SID) in cochannel speech, where two speakers are talking simultaneously over a single recording channel, is a challenging problem. Previous studies address this problem in the anechoic environment under the Gaussian mixture model (GMM) framework. On the other hand, cochannel SID in reverberant conditions has not been addressed. This paper studies cochannel SID in both anechoic and reverberant conditions. We first investigate GMM-based approaches and propose a combined system that integrates two cochannel SID methods. Second, we explore deep neural networks (DNNs) for cochannel SID and propose a DNN-based recognition system. Evaluation results demonstrate that our proposed systems significantly improve SID performance over recent approaches in both anechoic and reverberant conditions and various target-to-interferer ratios.
机译:同频道语音中的说话人识别(SID)是一个具有挑战性的问题,其中两个说话人通过一个录音通道同时讲话。先前的研究在高斯混合模型(GMM)框架下的消声环境中解决了这个问题。另一方面,在混响条件下的同信道SID尚未解决。本文研究了在回声和混响条件下的同频道SID。我们首先研究基于GMM的方法,并提出一个结合了两个同频道SID方法的组合系统。其次,我们探索用于同频道SID的深度神经网络(DNN),并提出了一种基于DNN的识别系统。评估结果表明,我们提出的系统在消声和混响条件以及各种目标干扰比下,都比最近的方法显着提高了SID性能。

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