In the recent years, extensive researches have been performed on various possible implementations of speaker diarization systems. These systems require efficient clustering algorithms in order to improve their performances in real-time processing. Teaching–learning-based optimization (TLBO) is such clustering algorithm which can be used to resolve the problem to the optimum clustering in a reasonable time. In this paper, a real-time implementation of speaker diarization (SD) system on raspberry pi 3 (RPi 3) using TLBO technique as classifier has been performed. This system has been evaluated on broadcasting radio dataset (NDTV), and the experimental tests have shown that this technique has succeeded to achieve acceptable performances in terms of diarization error rate (DER = 21.90% and 35% in single- and cross-show diarization, respectively), accuracy (87.30%), and real-time factor (RTF = 2.40). Also, we have tested TLBO technique on a 2.4 GHz Intel Core i5 processor using REPERE corpus. Thus, ameliorated results have been obtained in terms of execution time (xRT) and DER in both tasks of single- and cross-show speaker diarization (0.08 and 0.095, and 18.50% and 26.30%, respectively).

中文翻译：

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使用TLBO聚类算法在Raspberry PI3上实时实现说话人分类系统

近年来，人们对说话人分类系统的各种可能实现方式进行了广泛的研究。这些系统需要高效的聚类算法以提高它们在实时处理中的性能。基于教学的优化（TLBO）就是这样一种聚类算法，可以在合理的时间内将问题解决到最佳聚类。在本文中，使用 TLBO 技术作为分类器在树莓派 3 (RPi 3) 上实时实现说话人分类 (SD) 系统。该系统已在广播无线电数据集（NDTV）上进行了评估，实验测试表明，该技术已成功实现了可接受的二值化错误率性能（DER = 21.90% 和 35% 在单播和跨节目二值化， 分别），准确度 (87.30%) 和实时系数 (RTF = 2.40)。此外，我们还使用 REPERE 语料库在 2.4 GHz Intel Core i5 处理器上测试了 TLBO 技术。因此，在单场和跨场演讲者二分类任务中的执行时间 (xRT) 和 DER 方面都获得了改善的结果（分别为 0.08 和 0.095，以及 18.50% 和 26.30%）。