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Speaker segmentation is an important task in speech processing that involves identifying the boundaries between different speakers in an audio or video recording. The objective of speaker segmentation is to separate the speech of different speakers and assign each segment of speech to the appropriate speaker. Speaker segmentation is a challenging task due to the variability in speech signals caused by different speakers, acoustic conditions, and languages. In this project, we propose a speaker segmentation algorithm based on the clustering technique. The algorithm uses a set of acoustic features extracted from the speech signal to cluster speech segments belonging to the same speaker. We evaluate the proposed algorithm on a dataset of speech recordings and compare its performance with that of other state-of-theart speaker segmentation algorithms. The results show that the proposed algorithm outperforms the other algorithms in terms of accuracy and robustness. The proposed algorithm has the potential to be used in a wide range of speech processing applications, such as speaker diarization, automatic transcription, and speaker recognition.