• Dasari Indu
• Y.SRINIVAS

: In essence, speaker diarization provides a solution to the issue of "who spoke when" by labelling audio or video recordings with the speaker's identity. These algorithms, which were first created for voice recognition in multispeaker recordings, were later modified for speaker-specific metainformation, improving uses such as audio retrieval. Significant progress has been made in speaker diarization since deep learning was introduced. This study examines the development of speaker diarization historically as well as contemporary advancements in neural techniques. It also looks at how deep learning combines voice recognition and speaker diarization, showing how these two functions work best together. By combining neural techniques with modern advancements, this survey seeks to benefit the community by promoting speaker diarization efficiency.

Speaker diarization, speaker segmentation and clustering.

" Segmentation And Identification Of Voices Using Speaker Diarization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.e811-e816, December-2023, Available :http://www.jetir.org/papers/JETIR2312498.pdf

" Segmentation And Identification Of Voices Using Speaker Diarization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppe811-e816, December-2023, Available at : http://www.jetir.org/papers/JETIR2312498.pdf