End to End Neural Diarization

End-to-end neural diarization (EEND) aims to automatically segment and label audio recordings by speaker, directly predicting speaker identities without intermediate steps like clustering. Current research focuses on improving model architectures, such as those employing transformer networks, encoder-decoder attractors, and masked attention mechanisms, to enhance accuracy, particularly in handling overlapping speech and variable numbers of speakers. These advancements are significant because they streamline the diarization process, leading to more efficient and robust systems with applications in various fields, including meeting transcription, voice assistants, and forensic audio analysis.