Real-Time Implementation of Speaker Diarization System on Raspberry PI3 Using TLBO Clustering Algorithm

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).