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Majority of speech signals across different scenarios are never available with well-defined audio segments containing only a single speaker. A typical conversation between two speakers consists of segments where their voices overlap, interrupt each other or halt their speech in between multiple sentences. Recent advancements in diarization technology leverage neural network-based approaches to improvise multiple subsystems of speaker diarization system comprising of extracting segment-wise embedding features and detecting changes in the speaker during conversation. However, to identify speaker through clustering, models depend on methodologies like PLDA to generate similarity measure between two extracted segments from a given conversational audio. Since these algorithms ignore the temporal structure of conversations, they tend to achieve a higher Diarization Error Rate (DER), thus leading to misdetections both in terms of speaker and change identification. Therefore, to compare similarity of two speech segme
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