Abstract:
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.
