Speaker Diarization: A Comprehensive Survey of Clustering Methods, Neural Networks and Hybrid Frameworks for Robust Speaker Segmentation and Identification

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 1

Year of Publication : 2026

Authors : Merlin Revathy S, Kumar S S

10.14445/23488549/IJECE-V13I1P117

How to Cite?

Merlin Revathy S, Kumar S S, "Speaker Diarization: A Comprehensive Survey of Clustering Methods, Neural Networks and Hybrid Frameworks for Robust Speaker Segmentation and Identification," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 1, pp. 207-240, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I1P117

Abstract:

The speaker diarization involves splitting an audio recording into segments based on who is speaking and when they speak. In simple words, it helps you know, “Who spoke when?” This method of sorting voices is significant for various aspects of real life. People use it in automatic meeting notes, video subtitles, voice helpers, and legal or medical recordings. With this, you can see which person is always speaking, so it is easier to follow and understand the talk. Early systems depended on the handcrafted features, which performed well, but they faced challenges in the real world. Mostly, in the time of background noise, many speakers are talking at the same time, or speakers have similar voices. This survey paper provides an extensive review of 95 research papers published between 2023 and 2025, categorizing the existing diarization methods into three groups: supervised learning-based techniques, unsupervised learning-based techniques, and hybrid learning frameworks. Furthermore, the enhancement of diarization performance is facilitated by integrating speaker diarization methods with voice recognition applications. This paper offers a useful review by combining the most recent advancements with neural techniques, hence promoting further advancements in the direction of more effective speaker diarization.

Keywords:

CClustering Techniques, Deep Learning, Hybrid Frameworks, Multimodal Analysis, Speaker Diarization.

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